Nonlinear adaptive control of an underwater towed vehicle

Nonlinear adaptive control of an underwater towed vehicle Francisco Curado Teixeiraa,∗, António Pedro Aguiarb , António Pascoalb a Center for Environmental and Marine Studies and Dept. GeoSciences, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal b Institute for Systems and Robotics and Dept. Electrical Engineering, Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal Abstract This paper addresses the problem of simultaneous depth tracking and attitude control of an underwater towed vehicle. The system proposed uses a two-stage towing arrangement that includes a long primary cable, a gravitic depressor, and a secondary cable. The towfish motion induced by wave driven disturbances in both the vertical and horizontal planes is described using an empirical model of the depressor motion and a spring-damper model of the secondary cable. A nonlinear, Lyapunov-based, adaptive output feedback control law is designed and shown to regulate pitch, yaw, and depth tracking errors to zero. The controller is designed to operate in the presence of plant parameter uncertainty. When subjected to bounded external disturbances, the tracking errors converge to a neighbourhood of the origin that can be made arbitrarily small. In the implementation proposed, a nonlinear observer is used to estimate the linear velocities used by the controller thus dispensing with the need for costly sensor suites. The results obtained with computer simulations show that the controlled system exhibits good performance about different operating conditions when subjected to sea-wave driven disturbances and in the presence of sensor noise. The system holds promise for application in oceanographic missions that require depth tracking or bottom-following combined with precise vehicle attitude control. Keywords: Nonlinear control; adaptive control; Lyapunov methods; trajectory tracking; underwater; towed vehicle. 1. Introduction Underwater towed vehicles (tow-fishes) have found widespread utilization in the acquisition of oceanographic data. In many missions of interest, the vehicles are passive and their motion is controlled via a cable connecting them to a support ship. However, increasingly demanding missions scenarios require far better control of the tow-fish underwater, including simultaneous depth and precise attitude control under environmental disturbances. For this reason, there is currently increasing interest in the development of controlled underwater towed vehicles. In terms of depth control, two related problems may be posed. The first arises in the scope of physical and biological oceanographic missions and consists of tracking a depth profile which is independent of the topography of the sea-floor. The second problem arises in the context of bottom-following missions and ∗ Corresponding author: Phone +351 234370357, Fax +351 234370605 Email addresses: fcurado@ua.pt (Francisco Curado Teixeira), pedro@isr.ist.utl.pt (António Pedro Aguiar), antonio@isr.ist.utl.pt (António Pascoal) Preprint submitted to Ocean Engineering April 30, 2010 consists of maintaining a desired altitude above the sea-bottom; the latter is a common requirement, in particular for marine geophysical data acquisition. Simultaneous depth and attitude control is normally required since in some applications the attitude of the towfish may affect significantly the quality of the data acquired. 1.1. Applications and main motivation Applications of underwater towed vehicles in physical oceanography include the deployment of current profilers for sampling of small-scale ocean turbulence; see e.g. Gargett (1994), Spain and Fortin (1994). In this type of application, stabilization of the towfish in pitch and roll is necessary to guarantee good data accuracy; yaw disturbances can be tolerated provided that they do not induce significant pitch and roll motions that would violate the tilt specifications imposed by the acoustic methods (Schuch et al. (2005)). In marine geophysics, common applications include the use of side-scan-sonar systems, and marine vector magnetometers and gravimeters; see for example Parker (1997); Zumberge et al. (1997); Tivey et al. (1998). For sonar imaging, pitch and yaw rates may have a greater impact on image distortion than pitch and yaw angles since distortion depends on the change of attitude during the short time interval in which the image is recorded. The problems associated with side-scan sonar acquisition are analysed by Preston (1992); Preston and Shupe (1993); Preston and Poeckert (1993). It is shown in these references that even small errors in yaw can distort severely the sonar images and make the detection of small objects impossible. Although post-processing techniques can be applied to remove some of the deleterious effects, their effectiveness is limited by the degree of distortion imparted on the acoustic imagery by towfish motion. An analysis of the combined effects of yaw and pitch motions and the results of application of real time and offline correction methods to multibeam sonar data is presented by Cappell et al. (1993). The study demonstrates the limitations of the methods when applied to very wide swath multibeam data. The problem that we address was primarily motivated by the need to acquire vectorial measurements of the geomagnetic field and geomagnetic gradients close to the sea-bottom using marine vector magnetometers. Marine magnetic surveying is a very important tool in geophysical exploration. It is routinely applied to oil exploration to locate oil traps within sediments or topographic features of the basement that can influence the overlying sedimentary cover. Magnetometry is also an important tool in pipeline detection and tracking, mine hunting, and marine archaeology, among others. The latter applications normally involve small magnitude anomalies and require good signal-to-noise ratio. Marine magnetometers are typically towed by research vessels as a form of mitigating the effects of noise induced by the ship. Another reason to deploy the magnetometers in a towed platform is the ability to perform near-bottom surveys in deep waters. This is justified because the amplitude of magnetic signals generated by shallow geological bodies is sharply attenuated with increasing distance to the sources of the anomalies. For this reason, it is convenient for many applications to execute the survey as close to the sea-bottom as possible, employing a vehicle that can follow the terrain topography. More recently this type of surveys has started to be executed by ROVs and AUVs; see Tivey et al. (1998); Tivey and Johnson (2002). However, due mainly to the noise introduced by the vehicle thrusters this method incurs 2 a significant reduction of the signal-to-noise ratio. Hence, towing the magnetometer by a ship or an AUV is still the preferred method to locate the sensors away from the main sources of magnetic noise. It is well known that the accuracy of measurements of the geomagnetic field vector is largely dictated by orientation errors due to the large differences of the components of the Earth’s background field. Since magnetic gradient measurements are much less affected by sensor misalignments and orientation errors, in many applications it is convenient to measure the geomagnetic gradient or one of its components. In marine applications it is normally preferable to measure the vertical gradient since it does not impose any restriction on the heading of the sensors and is considerably more affordable than measuring the three orthogonal components. However, to measure the vertical gradient with high accuracy it is necessary to use a platform stabilized in pitch and roll. Motivated by the above problems, this paper addresses the simultaneous problem of depth and attitude control of an underwater towed vehicle. The organization of the paper is as follows: in the remainder of this Section we present prior and related work and summarize the proposed approach as well as our main contributions. Section 2 introduces the main physical characteristics of the system under study and the desired performance specifications, as well as the models of the towing arrangement and external disturbances. Section 3 describes the dynamical model of the vehicle and formulates the problem of depth tracking and attitude control in the presence of external disturbances and parametric model uncertainty. In Section 4, a solution to this problem is proposed in terms of a nonlinear adaptive control law. Section 5 presents a nonlinear observer for estimation of the heave velocity used by the controller, assuming that no direct measurements of surge, sway, and heave velocities of the vehicle are available for feedback. Section 6 evaluates the performance of the control algorithms developed using computer simulations. Finally, Section 7 contains some concluding remarks and proposes some guidelines for further research. 1.2. Prior and related work 1.2.1. Towing arrangements Towing systems play a crucial role in marine operations. For this reason, the last decades have witnessed considerable research work on the dynamics of towing cables and towed vehicles. See for example Abkowitz (1969) for an introductory exposition of this topic. Towing systems can be classified into two main types: single-part and two-part. Distinct models are needed to capture their dynamic behaviour. In the first type of arrangement, the dominant motion of the towing system is that due to the coupling between the ship and the towing cable. For this reason, considerable effort has been devoted to the study of towing cable dynamics. See e.g. Kato (1987); Kato et al. (1986); Joannides and Le Gland (1993); Yokobiki (2000); Yamaguchi et al. (2000); Buckham et al. (2003); Toda (2005) and the references therein. The need to determine accurately the position of a towed vehicle at the end of a long towing cable constitutes another motivation to model the behaviour of towing cables. The latter subject is addressed in Perrault et al. (1997) and Damy et al. (1994). Modelling is restricted to the vertical plane. Damy et al. (1994) propose a model for the towing cable to transform position measurements of the towing vessel obtained by GPS into estimates of position and velocity of the 3 towed vehicle. Calkins (1999) studies the lateral motion of a towed body in response to ship manoeuvres. Most of these studies adopt the lumped mass approach to model long towing cables. Although characterized by greater complexity of deployment, two-part towing arrangements (see Fig. 1) achieve superior performance in terms of rejection of the disturbances transmitted to the towing system. A study of this type of system is presented in Preston (1992), where the effects of wave-driven motions of the towing vessel on a sonar platform are discussed. The results of sea-trials demonstrate that a two-stage towing arrangement can significantly attenuate towfish attitude disturbances due to the surface ship motion. The performance of this type of towing arrangement is analysed in more recent research work that addresses the problem of towfish control; see Wu and Chwang (2000, 2001a,b).In particular, Wu and Chwang (2001b) propose a numerical model to investigate the hydrodynamic behaviour of an underwater vehicle in a two-part towing arrangement where the depressor is equipped with active horizontal and vertical control surfaces. 1.2.2. Control problem formulations In the literature, two main approaches can be found to the problem of towfish control: one addresses the problem only in the vertical plane (including depth and pitch); the second addresses control in both the vertical and horizontal planes (including control of depth, pitch, yaw, and lateral position). In general, the problem of motion in roll is neglected. Alternatively, roll stability is ensured through a combination of hydrodynamic damping and placement of the center of buoyancy of the vehicle (Wu et al. (2005)). For several marine applications, controlling the depth and the attitude of the vehicle in the vertical plane is sufficient. This approach is adopted by several authors; see e.g. Yamaguchi et al. (2000); Yokobiki (2000); Wu et al. (2005). A distinct approach to pitch stabilization is adopted by Woolsey and Gargett (2002) who propose a towfish equipped with a servo-actuated mass that can trim the vehicle’s center of gravity. This solution is practical at very low towing speeds, when the actuated control surfaces become ineffective. The problem of simultaneous control in vertical and horizontal planes is addressed in Wu and Chwang (2001b) but the controller proposed is not designed to react to perturbations in the horizontal plane. Furthermore, the vertical control surfaces are used only to direct the vehicle along a desired path. As a consequence, the performance achieved in yaw assessed in simulation is less than satisfactory. In Kato (1991) the problem of controlling the attitude, altitude, and lateral position of an underwater vehicle towed with a single cable is addressed using a decoupled model of vertical and horizontal motions. Encouraging results are obtained with the controlled system simulated under the influence of disturbances caused by sea waves and tidal currents. A fully 3D-control of an hybrid Autonomous Underwater Vehicle (AUV)/towfish system is addressed in Choi et al. (2005). Recent studies include other non conventional approaches such as towing by an AUV or an Autonomous Surface Vehicle (ASV); see Buckham et al. (2003) and Lambert et al. (2003). In some cases, the towed vehicle is equipped with thrusters to enhance the control capabilities of the system; see Wu et al. (2005). 4 1.2.3. Controller design and implementation methods In terms of controller design, the methods normally employed for towed vehicle control assume that the towfish is operated close to some equilibrium point and rely on the linearisation of the vehicle dynamics about that point. In most of the work cited in the literature, the authors resort to PID control. Campa et al. (1996) propose a model-based controller that addresses robustness issues such as uncertainties in the determination of system parameters and errors introduced by linearisation of the system dynamics in a �∞ framework. This work reports the same problem mentioned in Perrault et al. (1997): there is a tradeoff between good altitude control and pitch control. The problem of depth control for a direct tow is addressed by Nasuno et al. (2008) resorting to Linear Matrix Inequalities (LMIs). The controller derived is suitable for different equilibrium conditions under the assumption of small heave forces and small cable vibration disturbances. 1.2.4. Experiment based modeling Since in the model proposed in this study the motion of the towfish is derived from the motion of the gravitic depressor, we emphasize the importance of using a realistic simulation of depressor motion. To assess the consistency of the approach presented in this work we studied other models of depressor motion described in the literature and analysed how closely our model reproduces their theoretical and experimental results. Particularly relevant for the present work are the results reported by Preston (1989), Preston (1992) and Preston and Shupe (1993), obtained in trials at sea with different types of towing arrangements and towed vehicles. The experimental results obtained by Wu and Chwang (2001a) with a scaled model and by Hopkin et al. (1993) with full-scale experiments in a towing tank constitute also important references in the present context. These results will be discussed in Section 2. 1.3. Proposed approach and main contributions of the work One of the main drawbacks of the aforementioned controller design formulations stems from the fact that they rely on linearisation of the system dynamics in the neighbourhood of a few operating points. This approach has limited applicability to the solution of the problem at hand since the motion of the underwater towed vehicle can be largely influenced by external disturbances resulting in large excursions over a wide range of operating conditions. As a contribution to overcoming well known limitations, in the present work the problem of towfish control is addressed in the scope of nonlinear control theory. The study that we perform resorts to nonlinear model-based design and employs stability analysis tools to handle the nonlinearities of the system in a large operation range. This approach was dictated mainly by the requirement that the controller should yield good performance when the vehicle undergoes motions about different equilibrium conditions and exhibit robustness against vehicle parameter uncertainty. The equilibrium conditions are determined by, among other factors, the pigtail length and the towing speed. The vehicle dynamic model adopted builds on previous work in Teixeira et al. (2006). However, in the present version we tackle the problem of controlling the vehicle’s depth and attitude in both the vertical and horizontal plane, not just in the vertical plane. The key contributions of the present paper are twofold: 5 1. An adaptive control structure is proposed that exhibits good performance about different equilibrium conditions and is robust against vehicle parameter uncertainty. The nonlinear control law derived is proven to stabilize the system in the presence of bounded external disturbances and unmodeled dynamics. 2. Controller design assumes explicitly that no direct measurements are available of surge, sway, and heave speeds. In contrast with previous work in Teixeira et al. (2006), a nonlinear observer is used to estimate these variables. 2. System characteristics and modeling 2.1. System characteristics In the present study the towfish specification and key towfish hydrodynamic parameters are derived from the configuration proposed in Schuch (2004) and Schuch et al. (2005). Additional parameters are based on those commonly used in the design of underwater vehicles; see e.g. Pascoal et al. (1997), Silvestre and Pascoal (1997)). For safety reasons, the towfish has slightly positive buoyancy. 2.1.1. Towing system The towing system that we consider consists of a two-part towing arrangement as illustrated in Fig. 1. The nose of the towfish is connected via a small umbilical (the pigtail section) to a gravitic depressor, which is in turn connected to and towed by a support ship using a long tow line (the primary cable). 2.1.2. Vehicle characteristics The proposed vehicle is equipped with a bow plane, a stern plane, and a rudder. The bow and stern control planes are equidistant from the vehicle’s center of mass; see Fig. 2. The sensor suite proposed includes an Attitude and Heading Unit (AHU), a depth sensor, and optionally a sonar altimeter. The towfish has a slightly positive buoyancy and the metacentric height is such that the vehicle is naturally stabilized in roll. The towing arrangement proposed includes a communications’ cable between the towed vehicle and the towing vessel for transmission of control signals and data. Although in some mission scenarios the system proposed may dispense with the need for a cabled communication link between the towfish and the support vessel, the classical cabling solution is recommended to allow for real-time monitoring of system operation and on-board data recording. 2.2. Performance specifications The system’s performance specifications are dictated by the requirements imposed by different mission scenarios that include geomagnetic surveying, deployment of current profilers, and sonar imaging. The specification for the maximum depth error is dictated by the desired bound on the variations of the magnetic readings caused by variations of the surveying altitude. The errors so originated are very difficult to correct in post-processing due to the nonlinear character of potential fields’ scaling; see e.g. Blakely (1995). Based on the requirements of some typical scenarios of operation we require the towed vehicle to regulate the depth error to zero ±0.30�. This specification is justified in Appendix A. The tilt 6 specifications for the proposed towed vehicle are dictated by the requirements of the acoustic methods used in sonar imaging applications and physical oceanography, cf. Preston (1992), Schuch et al. (2005). The controller is required to regulate the pitch angle and the yaw error to zero ±1 deg while the towing system and the vehicle are designed to passively stabilize the attitude in roll. The towing speed is expected to vary between 3knts and 8knts, depending on the type of oceanographic mission. 2.3. Analysis of previous towing models The sheathed model proposed by Chapman (1982), which approximates the behaviour of a Bowden cable, decomposes motions of the top of the main cable into two components - parallel and transverse to the upper cable segment. That model is applied to determine the motion of 100m of faired cable where the cable top (at the vessel towing point) is subjected to periodic oscillations. The simulations results show that after a certain time the fish adopts a cyclic motion; the locus of the bottom end of the cable approximates an ellipse with a large eccentricity. The ratio of the ellipse’s minor axis to the ship motion amplitude is determined for different periods of oscillation. The work considers a neutrally-stable body with isotropic drag that behaves like a sphere with constant drag coefficient; this corresponds to the configuration of our gravitic depressor. According to the authors, the Bowden cable behaviour provides a very good approximation to the response of a towed weight to random disturbances. Hubbard (1993) shows that the most effective form of mitigating disturbance propagation through the towing cables consists in forcing the lower towline axis (either in a single tow or in a two-part tow) to be coaxial with the towfish axis of symmetry. To implement this procedure, an approximately neutrally buoyant towfish is required. With this method, the axial motion of the towline produces dominantly fore-aft accelerations of the towed vehicle. The model of the wave-induced disturbances that we adopt takes in consideration the results mentioned below. Preston and Shupe (1993) show that heave of the towing point in different ships is the variable that is least dependent on the encounter angle while sway is approximately equal in head and following seas and surge is the most dependent on ship configuration. Wu and Chwang (2000) conclude that the two-part tow method has practically no influence on the surge and sway behaviour of the towfish. They show that the heaving ratio between the towed vehicle and the depressor depends on the encountering frequency of the towing ship and the length of the secondary cable. The ratio decreases for increasing encountering frequency and increasing secondary cable length. Hence, Wu and Chwang (2000) suggest that in order to improve the stability of a towed vehicle during survey operations it is advisable to select a sufficiently long secondary cable and to keep the towing vessel in a convenient wave aspect such as head sea towing. These conclusions are supported by the experimental results obtained by Hopkin et al. (1993) in tow tank experiments at full-scale. These results show also that the heave amplitude ratio increases with increasing towing speed. In this respect we notice an apparent contradiction in the conclusions drawn by Wu and Chwang (2001a) suggesting that the towfish/depressor heave ratio is smaller for lower towing speeds. Actually, a closer analysis of the experimental results presented in Wu and Chwang (2001a) shows that for towing speeds larger than 1.5m/s the heaving ratio effectively increases with increasing towing speed. 7 2.4. Notation In what follows, {�} represents an inertial coordinate frame and {�} denotes a body-fixed frame that moves with the vehicle. The referential associated with the secondary cable that is defined in a forthcoming section is denoted {�}. Following standard notation (Fossen (2002)), the vector � := [�, �, �, �, �, �] represents the position and orientation of the vehicle expressed in {�} and � := [�, �, �, �, �, �] denotes the linear and angular components of the velocity of the vehicle expressed in {�}. When convenient, we also use vectors �1 := [�, �, �] to represent the position of the origin of {�} expressed in {�} and �2 := [�, �, �] to represent the Euler angles. The velocity vector is also usually decomposed into the linear velocities represented by �1 := [�, �, �] and the angular velocities represented by �2 := [�, �, �]. Whenever necessary, to simplify the notation the following abbreviations are used: � ⋅ := sin(⋅), � ⋅ := cos(⋅), and � ⋅ := tan(⋅). The symbol × is used to denote vector cross product. The matrix trace operator is denoted by tr(⋅). A list of the symbols used in the paper is presented in Tables 1 to 4. Refer also to Tables 5 and 6 for a list of other variables used in simulations. 2.5. 6DOF Equations of motion The simulation of the vehicle dynamics is implemented using the three-dimensional 6��� equations of motion represented in compact form by (see e.g. Fossen (2002)): � �˙ + �(�)� + �(�)� + �(�) = � + �� (1) �˙ = �(�2 )� where � is the system inertia matrix, �(�) is the Coriolis and centripetal matrix, �(�) is hydrodynamic damping matrix, and �(�) represents the transformation matrix from the body-fixed frame {�} to the inertial coordinate frame {�}. The forces and moments due to actuation of the control surfaces are represented by � . The vector �� represents the external forces and moments acting on the system expressed in the body-fixed referential, including the effects of the towing forces and moments, and the external disturbances. The expanded model is presented in Appendix B. 2.6. Towing system and wave models In the present study, the towing system is simulated in a simplified manner by assuming that the perturbations induced by sea-waves are transmitted to the depressor without attenuation. Thus, the motion of the depressor corresponds to the motion of the towing point at the towing ship. The cable section connecting the depressor to the towfish is modeled as a spring-damper system similar to the one proposed by Schuch (2004) where the spring constant is a function of cable characteristics while damping is imposed by the towfish. Although this is a simplified model it can be interpreted as a “worst case” scenario. In this towing arrangement, the length of the pigtail can be used as a design parameter to filter the effects of the disturbances transmitted to the depressor through the primary cable. 2.6.1. Modeling cable tension forces Consider the two-part towing system with a secondary towing cable connecting directly the depressor weight to the nose of the towfish as illustrated in Fig. 3. A cable-fixed reference frame {�} is defined 8 having its x-axis collinear with the secondary cable and pointing in the direction of the depressor. The origin of {�} may be chosen as any point along the cable. The orientation of {�} relative to {�} is determined by a transformation defined by a sequence of two rotations: a rotation over yaw angle �� about the z-axis followed by a rotation over pitch angle �� about �� . The forces applied to the depressor give rise to the force �� along the cable, actuating at the towfish towing point according to the model presented in Appendix C; see Fig. 3. When the distance ∣Δ�∣ between the depressor and the towing point is smaller than the cable length �� , the forces actuating at the depressor are not transmitted to the towed vehicle. This model is suitable to generate (realistic) impulsive forces that affect the stability of the towfish. Hence, the vector �� of external forces and moments expressed in the body-fixed referential is determined by �� := [��� , ��� ]′ where ��� ⎧ ( ) ⎨ �� � �� � � � � := ⎩ 0 ⇐ ∣Δ�∣ > �� ⇐ ∣Δ�∣ ≤ �� ��� := [�� /2, 0, 0]′ × ��� � � with �� denoting the length of the vehicle. �� and �� are the rotation matrices from {�} to {�} and from {�} to {�}, respectively. 2.6.2. Modeling wave-driven disturbances To capture a realistic scenario, the wave induced perturbations are modelled according to the JONSWAP wave spectrum (see e.g. Fossen (2002)). The spectral density function of the wave model used in the simulations is illustrated in Fig. 5. The peak frequency used in the model is computed as the frequency of encounter �� , defined by     �2 �� (�, �0 , �� ) := �0 − 0 � cos ��  � where �0 is the modal frequency of the JONSWAP wave spectrum, � is the surge speed of the surface vessel, and �� is the angle of encounter defined between the ship’s heading and the direction of the incident waves; see Fig. 4. In this work, the towing vessel is assumed to be operated in head seas. The model adopted for wave-driven disturbances decomposes the ship motion that drives the oscillations of the gravitic depressor into three components of which heave is the most significant one. To implement this approach the sea wave amplitude determined by the JONSWAP wave model is decomposed into the three orthogonal components of motion and has a different phase delay associated to each direction. The relative motion amplitude in each direction is defined by the parameters �� , �� , and �� and the corresponding phases are represented by �� , �� , and �� , respectively; see Table 6. The perturbation model used includes a low-frequency component in sway similar to that proposed by Wu and Chwang 9 (2000). In modeling the heave component we take in consideration the experimental results obtained by Preston and Shupe (1993). These results show that the significant means of the towing vessel’s vertical and horizontal displacements in head seas is nearly proportional to the significant wave height. 3. Control problem formulation In this section we describe the dynamical model of the vehicle and formulate the problem of depth tracking and attitude control, in the presence of external disturbances and parametric model uncertainty. At a first stage the formulation is presented neglecting the motion in roll. The impact of the latter on the vehicle dynamics and control performance will be examined at a later stage. The coupling terms of the system dynamics resulting from the motion in roll will be introduced in subsection 4.3. 3.1. Simplified equations of motion used for controller design 3.1.1. Kinematics The pose (position and attitude) of the vehicle relative to {�} is represented by vector �1 := [�, �, �]′ . The velocity vector, that includes heave velocity and pitch and yaw rates expressed in {�} is �2 := [�, �, �]′ . The simplified kinematic model based on the assumption that the vehicle is stable in roll is: �˙ = −� sin � + � cos �, �˙ = �, (2) �˙ = �/ cos �. 3.1.2. Dynamics The relevant equations of the simplified body-fixed dynamics are given by (� ¯ − ��˙ )�˙ − ��˙ �˙ − ��˙ �˙ − (� ¯ − ��˙ )�� −�∣�∣� ∣�∣� + �� (�1 , �, �) + �� (�) = (3) �¯� (�, �, �) + �� , (��� − ��˙ )�˙ − ��˙ �˙ − ��˙ �˙ +(��˙ − ��˙ )�� − ��� �2 − �∣�∣� ∣�∣� + �� (�1 , �, �) (4) +�� (�) = �¯� (�, �) + �� , (��� − ��˙ )�˙ − ��˙ �˙ +(��˙ − ��˙ )�� + ��� �� − �∣�∣� ∣�∣� + �� (�1 , �, �) (5) =� ¯� (�, �, �) + �� . where � := [�� , �� , �� ]′ is the vector of deflections of the three control surfaces (bow, stern elevators, ( ) and rudder) and � and � represent the angles of attack and side-slip, defined as � := tan−1 � and � ( √ ) −1 2 2 � := tan �/ � + � , respectively. The variables �� , �� , and �� denote external forces and moments resulting from the towing forces and external disturbances. The functions � ¯� (.), � ¯� (.), and �¯� (.) represent the forces and moments due to the common mode (�� + �� ), differential mode (�� − �� ), and rudder (�� ) control actions, respectively. The remaining functions, �� (.),�� (.), and �� (.) are hydrodynamic forces 10 and moments associated with heave, pitch and yaw, respectively; �� (.) and �� (.) denote the restoring forces and moments. The constants ��˙ , ��˙ , ��˙ , ��˙ , ��˙ , ��˙ , ��˙ , ��˙ , �∣�∣� , �∣�∣� , and �∣�∣� represent system coefficients according to the notation of SNAME (1950). The meaning and values of the system’s parameters are presented in Table 5. 3.1.3. Linearly parametrized system To cast the system in the form of a linear parametric model, all the system parameters are lumped in matrix Γ which is derived below (refer to Tables 2 to 5 for the definitions of the vehicle parameters used in the forthcoming expressions). Define �1 := 1 , �−� ¯ � ˙ �2 := 1 (��� −��˙ ) , �3 := (6) 1 (��� −��˙ ) , 2 ¯ − �)] ¯ ′, ¯ − ��˙ ), �¯ 3 , (� Γ1 := �1 [�∣�∣� , (� ¯ ��� ]′ , Γ2 := �2 [�∣�∣� , (��˙ − ��˙ ), �¯ , �, (7) Γ3 := �3 [�∣�∣� , �, −��� ]′ , and take � := ����(�1 , �2 , �3 ) > 0. Consider also the following functions of the measured variables: �1 := [∣�∣�, ��, �� (�1 )�� (�, �), cos �]′ , �2 := [∣�∣�, ��, �� (�1 )�� (�, �), �� sin �, �2 ]′ , (8) �3 := [∣�∣�, �� (�1 )�� (�, �), ��]′ , with �� (�1 ) := 12 �(�2 + � 2 + �2 ), �� (�, �) := −���� �, �� (�, �) := −���� �, and �� (�, �) := ���� �. Notice that the terms involving parameters ��˙ , ��˙ , ��˙ , and ��˙ have been eliminated in the above expressions. These constants correspond to off-diagonal elements of the added-mass matrix that can be neglected (see Fossen (2002)). Using the above definitions and making Γ := ����(Γ′1 , Γ′2 , Γ′3 ), Σ := [�1′ , �2′ , �3′ ]′ , and � := ΓΣ, the system dynamics (3),(4), and (5) admit the representation �˙ 2 = � + �¯ �+� (9) where � := �[�� , �� , �� ]′ represents the effect of the external forces and moments and the control actions are represented by � ¯ := [¯ �� , � ¯� , � ¯� ]′ . The simplified kinematics are represented by �˙ 1 = � −1 (�2 − �) (10) where � := ����(1/ cos �, 1, cos �) > 0 and � := [� tan �, 0, 0]′ with ∣�∣ < �/2. 3.2. Control problem formulation Let �� : [0, ∞) → IR be a sufficiently smooth time-varying depth reference trajectory with a uniformly bounded time-derivative and let the desired pitch and yaw angles be �� and �� , respectively. Define also the tracking error as �¯ := �1 − �1� = [� − �� , � − �� , � − �� ]′ . The problem we address can be formally 11 posed as follows: Consider the state-space model �˙ 1 = �1 (�1 , �2 ), �1 = ℎ1 (�1 , �1 ); �˙ 2 = �2 (�2 , �1 , �� ), �2 = ℎ2 (�2 , �1 ); where �1 (.) denotes the kinematic model represented by (10), �2 (.) denotes the dynamic model represented by (9), �1 and �2 are the measured outputs, ℎ1 (.) and ℎ2 (.) denote measurement functions or observers, and �� denotes the external forces affecting the system. Design an output-feedback control law such that all closed-loop signals are bounded and the tracking error norm ∥¯ �∥ converges exponentially fast to a neighbourhood of the origin that can be made arbitrarily small in the presence of parameter model uncertainty and bounded external disturbances. 4. Output feedback controller design To solve the trajectory tracking problem formulated above we propose a Lyapunov-based adaptive control law. Its derivation makes ample use of backstepping theory (see e.g. Khalil (2002)) and unfolds in the sequence of steps detailed below. 4.1. Nonlinear controller design Step1. Convergence of �¯. Consider the Lyapunov control function �1 := 21 �¯′ �¯, whose time-derivative is �˙ 1 = (�1 − �1� )′ (�˙ 1 − �˙ 1� ) = (�1 − �1� )′ (� −1 (�2 − �) − �˙ 1� ). (11) Using �2 as a virtual control input and setting �2 = �(�˙ 1� −�� �¯)+�, for some positive definite (p.d.) diagonal matrix �� makes �˙ 1 negative. Clearly, the desired trajectory represented by �1� is required to be bounded and twice time-differentiable. Define the error variable �¯ := �2 − �(�˙ 1� − �� �¯) − �. Then, (11) can be re-written as �˙ 1 = −¯ �′ �� �¯ + �¯′ � −1 �¯. (12) Step2. Backstepping for �. For simplicity of presentation, assume at this stage that there are no external disturbances. This assumption will be removed later. Under these conditions, � = 0 in (9). The dynamic equation of the error �¯ can then be written as ¨ 1� ) + �(� ˙ � �¯ − �˙ 1� ) − �, ˙ �¯˙ = � + �¯ � + �(�� �¯˙ − � (13) ¨ 1� is the second time-derivative of the reference trajectory. where � Define the augmented Lyapunov function 1 1 1 �2 := �1 + �¯′ �¯ = �¯′ �¯ + �¯′ �¯. 2 2 2 12 (14) Using (13), its time-derivative is ¨ 1� ) + �(� ˙ � �¯ − �˙ 1� ) − �]. �˙ 2 = −¯ ˙ �′ �� �¯ + �¯′ [� −1 �¯ + � + �¯ � + �(�� �¯˙ − � (15) The objective is to drive the term on �¯ to zero using the control � ¯. However, this is not practical to do because some of the parameters of the vehicle are not known with good accuracy. Hence, the variables � ˆ1 , � ˆ2 , � ˆ3 , Γ̂1 , Γ̂2 , and Γ̂3 are defined to represent estimates of �1 , �2 , �3 , Γ1 , Γ2 , and Γ3 respectively, and set the control using the estimated model parameters as ¨ 1� ) + �(� ˙ � �¯ − �˙ 1� ) − �˙ + �� �¯], � ¯ := −�ˆ−1 [� −1 �¯ + �ˆ + �(�� �¯˙ − � (16) where �� is a p.d. diagonal matrix, �ˆ := ����(ˆ �1 , � ˆ2 , � ˆ3 ), and �ˆ := Γ̂Σ = ����(Γ̂′1 , Γ̂′2 , Γ̂′3 )Σ. It is required ¨ 1� be bounded in order to guarantee boundedness of the control signal. Define also Π := [Γ, �]′ that � ˆ Γ̃ := Γ − Γ̂, and Π̃ := Π − Π̂ with Π̂ := [Γ̂, �] ˆ ′ , and take and the estimation errors �˜ := � − �, ¨ 1� ) + �(� ˙ � �¯ − �˙ 1� ) − �˙ + �� �¯))′ ]′ . Using the control law (16), Φ := [Σ′ , −(�ˆ−1 (Γ̂Σ + � −1 �¯ + �(�� �¯˙ − � straightforward algebraic manipulations yield the time-derivative of �2 as ˜ Γ̂Σ + � −1 �¯ + �(�� �¯˙ − � ¨ 1� ) �˙ 2 = −¯ �′ �� �¯ − �¯′ �� �¯ + �¯′ [Γ̃Σ − �ˆ−1 �( ˙ � �¯ − �˙ 1� ) − �˙ + �� �¯)] +�(� (17) = −¯ �′ �� �¯ − �¯′ �� �¯ + �¯′ Π̃′ Φ. Step3. Adaptive control. At this stage we introduce a third Lyapunov function that captures the effect of the error in the estimation of the system’s parameters, �3 := �2 +   1 1  1  1  2  2 Π̃ = �¯′ �¯ + �¯′ �¯ + Π̃ 2� 2 2 2� � � (18) for some scalar � > 0, where ∥.∥� stands for the Frobenius norm. The time-derivative of �3 is 1 ˙ �˙ 3 = −¯ �′ �� �¯ − �¯′ �� �¯ + tr(Π̃′ (Φ¯ � ′ − Π̂)). � (19) Let Π̂0 represent an initial estimate of Π and let Π̃0 be an upper bound on the error Π − Π̂0 . Setting the dynamics of Π̂ as ˙ Π̂ = �[Φ¯ � ′ − �(Π̂ − Π̂0 )] (20) for some scalar � > 0 yields �˙ 3 = −¯ �′ �� �¯ − �¯′ �� �¯ + tr(�Π̃′ (Π̂ − Π̂0 )). Note that the effect of the scalar gain � in the adaptive control law (20) is to ensure that the values of the system parameters estimated by the adaptive controller remain inside a ball with the center defined 13 by the corresponding initial values. Applying the equality  2 2 1  1  1   2     tr(�Π̃′ (Π̂ − Π̂0 )) = − � Π̃ − � Π̂ − Π̂0  + � Π − Π̂0  2 2 2 � � � yields   1  1   2  2 �˙ 3 ≤ −¯ �′ �� �¯ − �¯′ �� �¯ − � Π̃ + � Π̃0  . 2 2 � � (21) Although it is not possible to ensure that �˙ 3 is always negative, it is shown in the forthcoming analysis that this is sufficient to ensure practical stability (Jiang et al. (1994)). External forces. At this stage the simplifying assumption made in Step 2 is removed to consider the external forces and moments represented by � in (9). Noting that these are unknown terms that cannot be taken into account in the control law (16), instead of (17) it follows �˙ 2 = −¯ �′ �� �¯ − �¯′ �� �¯ + �¯′ Π̃′ Φ + �¯′ � (22) and 4.2. Stability analysis   1  1   2  2 �˙ 3 ≤ −¯ �′ �� �¯ − �¯′ �� �¯ − � Π̃ + � Π̃0  + �¯′ �. 2 2 � � (23) The following theorem may now be formulated: Theorem I Consider the closed-loop system Ω consisting of the vehicle model (2),(3), (4), and (5) and the adaptive feedback controller (13), (16), and (20). Given a bounded, sufficiently smooth time-varying reference trajectory �1� : [0, ∞) → IR × (− �2 , �2 ) × [−�, �], the following holds: i) For any initial condition, the solution to Ω exists globally, all the closed-loop signals are bounded, and the tracking error �¯ satisfies ∥¯ �∥ ≤ �−�� �0 + � (24) where �, �0 , and � are positive constants and �0 depends on the initial conditions. ii) By appropriate choice of the controller parameter �� , the rate of convergence � and the radius � can be chosen at will. Proof From (23), using Young’s inequality 1 , follows that  1  � 1  2 �′ �� �¯ − �¯′ (�� − �)¯ �˙ 3 ≤ −¯ � − � Π̃ + Δ2 , 2 2 2 � for any scalar constant � > 0, where Δ = �� √ � + (25)  √    � Π̃0  , with �� := sup ∥�∥. From (18) and (25), and � �≥0 assuming that �� satisfies �� > �2 �, we conclude that there exists a constant � that verifies simultaneously 1 Young’s inequality states that if a and b are non negative real numbers and p and q are positive real numbers such that � � 2 2 1/p + 1/q = 1 then �� ≤ �� + �� . In particular, for any real number � > 0 �� ≤ �2� + ��2 . 14 0 < � ≤ ��, �� ≤ �� , and �� ≤ 2�� − ��, and therefore 1 �˙ 3 ≤ −��3 + Δ2 . 2 (26) Statement (i) is proven by applying the Comparison Lemma (Khalil (2002)) and showing that �3 (�) ≤ �−�� �3 (0) + 1 2 Δ 2� (27) along the solutions of Ω. This shows that all control signals remain bounded and the solutions of the 1 2 2� Δ . system exist globally and are ultimately bounded with ultimate bound �∥ converges to a ball of radius of �3 , we conclude from (27) that ∥¯ √Δ . 2� Considering the definition We can also conclude that the closed-loop system is input-to-state practically stable (ISpS) (Jiang et al. (1994)) with respect to bounded parametric uncertainties and bounded external disturbances. To prove (ii) it is shown that the radius √Δ 2� can be made arbitrarily small by appropriate choice of the controller parameters. For a given limiting radius � and a given convergence rate � �� Δ √ ≤�= √ + 2� 2�� and it is possible to make √  �    Π̃0  , 2� � √   )2 ( �   � := �2� /2� � − Π̃0  2� � provided that �� − � � = �� − 2 �2� �  ) � ≥ � > 0. ( √�    2 2 2� � − 2� Π̃0  � Thus, it is shown that (26) is verified and therefore (27) holds. 4.3. Coupling the horizontal and vertical planes To take in account the effects of motion in roll, the following coupling terms must be inserted in the dynamic equations of the system (see Appendix D): ¯ − �)] ¯ ′, Γ�1 := �1 [−(� ¯ − ��˙ ), (� Γ�2 := �2 [(��� − ��� + ��˙ − ��˙ ), −��� ]′ , Γ�3 := �3 [(��� − ��� + ��˙ − ��˙ ), ��� ]′ , ��1 := [��, cos �(���� − 1)]′ , ��2 := [��, �2 ]′ , ��3 := [��, �2 ]′ . The system dynamics can be reformulated by substituting the new expressions for Γ, Σ, �, and � as follows. Define Γ := ����([Γ′1 , Γ′�1 ], [Γ′2 , Γ′�2 ], [Γ′3 , Γ′�3 ]), 15 ′ ′ ′ ′ Σ := [�1′ , ��1 , �2′ , ��2 , �3′ , ��3 ], � := ����(�1 + ��1 , 1 + ��2 , 1/�1 + ��3 ), � := [�1 + ��1 , ��2 , ��3 ]′ , with �1 := 1/ cos �, �1 := � tan �, ��1 := (1 − cos �)/ cos � cos �, ��2 := (cos � − 1)/ cos �, ��3 := cos �(1 − 1/ cos �), ��1 := � tan �(1/ cos � − 1) − � tan �, ��2 := � tan �, and ��3 := −� tan �. Let the coupling components of the system dynamics be represented by Ω� := [Γ′�1 ��1 + ��1 , Γ′�2 ��2 + ��2 , Γ′�3 ��3 + ��3 ]′ (28) and let �� ,�� , and �� denote the upper bounds on the surge and sway velocities, and roll rate, respectively. Simple algebraic manipulations show that ∥Ω� ∥2 ≤ �� ∥�2 ∥2 + Δ� , (29) where �� and Δ� are constants defined from the system parameters and the upper bounds on the linear velocities, �� ,�� , and �� (cf. Appendix D). Inserting in (22) the coupling terms not compensated by the control �¯ we obtain �˙ 2 = −¯ �′ �� �¯ − �¯′ �� �¯ + �¯′ Π̃′ Φ + �¯′ � + �¯′ Ω� . (30) Furthermore, applying Young’s inequality we conclude from (25) that, for any scalar constant �� > 0, �˙ 3 verifies �˙ 3 ≤ −¯ �′ �� �¯ − �¯′ (�� − �� ∥�2 ∥2 + ∇ + 2� � where ∇ = 1 2 2 (Δ + Δ� �� ).  2 (�+�� ) 1   �)¯ � − � 2 2 Π̃� (31) It can be shown, applying a reasoning similar to the one used in Theorem I, that �3 (�) ≤ �−�� �3 (0) + �� (∥�2 ∥) + ∇ , � (32) where �� is a class K function (Khalil (2002)); see demonstration in Appendix D. Hence, we conclude that the system is ISpS with respect to bounded unmodeled dynamics. 5. Nonlinear observer design In actual implementations it is often impractical to obtain direct measurements of surge, sway, and heave for feedback. Taking this in consideration, for observer design purposes it is assumed that the surge velocity of the towed vehicle is constant and can be approximated by the nominal towing speed, i.e. � := �0 . Furthermore, the effect of the sway component is assumed to be negligible. The heave component of the vehicle’s velocity is estimated by a nonlinear observer that is described below. Consider the state space representation 16 � �˙ � = �� �� + �� (�, �, �� ) + �� (�� , �), (33) �� = �, ⎡ where �� := [�, �]′ , � := ����(1, �3,3), �� := ⎣ ⎡ �� (�, �, �� ) := ⎣ 0 cos � 0 �� ⎤ ⎦, −�0 sin � ¯ − �) ¯ cos � + �� (� ¯ − ��˙ )�0 � + �∣�∣� ∣�∣� + (� ⎤ ⎦, and �� (�� , �) := [0, − 21 ��� ��� (�20 + �2 )�� ]′ , with �� := �� + �� , �3,3 := � ¯ − ��˙ > 0, and �� := 2 − 21 �(�¯ 3 ���� + 2�� ��� )(�0 + 0.3) < 0. The variable �� represents the heave component of the external forces actuating on the towfish; see Section 3.1. Let �ˆ and � ˆ represent the estimated values of � and �, respectively, and define � ˜ := �−�. ˆ Here we use the approximation �� (�1 )�� (�, �) ≈ − 12 ����� (�0 +0.3)� that is justified in Appendix E. Applying the expression for the dynamics of � derived from (3) we implement an estimator of � in the form of a Luenberger observer �� ˆ˙ � = �� � ˆ� + �� (�, �, �� ) + �� (ˆ �� , �) + �� (�� − �ˆ� ), �ˆ� = ℎ′ � ˆ� = �ˆ, (34) where ℎ := [1, 0]′ and �� := [�1 , �2 ]′ is the gain of the observer, with the scalars �1 and �2 defined in the sequel of the present formulation. In our implementation, due to the configuration of the towfish and the towing arrangement, the magnitude of �� is very small relatively to the remaining terms in �� (.) and can �� , �) = [0, �� (�2 − � ˆ 2 )]′ be neglected. Using � ˜� := �� − �ˆ0 , �� := − 12 ��� ��� �� , and �� := �� (�� , �) − �� (ˆ the observer error dynamics become �� ˜˙ � = �� � ˜ � − � � ℎ′ � ˜� + �� . (35) Introduce now the Lyapunov function �� := � ˜′� � � ˜� . Its time derivative is �˙ � = −˜ �′� �� � ˜� + 2˜ �′� �� , (36) with �� := (�� ℎ′ + ℎ��′ − �� − ��′ ) > 0 for all �1 > 0, �2 ≥ 1. The heave velocity of the vehicle is assumed to be bounded. This assumption is a natural consequence of the towing arrangement and towfish configuration and is validated by the results of simulations. In fact, even when the system is subjected to fast heave movements of the depressor, the towfish tends to pitch rather than heave. Given an upper bound �� on ∣�∣, it is straightforward to show that (see Appendix E) �˙ � = −˜ �′� �� � ˜� + 2�� � ˜2 (� + �) ˆ ≤ −˜ �′� [�� − 2 ∣�� (�� + �)∣ ˆ �] � ˜� . (37) Notice that except for the diagonal element �22 > 0 the elements ��� (�, � = 1, 2) of matrix �� are determined by the observer gains �1 and �2 . Hence, to make �� − 2 ∣�� (�� + �)∣ ˆ � > 0 the following condition must hold: 17 ∣�� (�� + �)∣ ˆ < ∣�� ∣ . (38) Condition (38) establishes the region of attraction of the origin of � ˜� , which depends on the towing velocity as shown in Appendix E. Thus, although it is not possible to ensure global stability, through proper manipulation of the values of �1 and �2 the origin of � ˜� can be made semi-globally asymptotically stable (Khalil (2002)). 5.1. Stability of the observer with respect to measurement noise Consider now that the measurements of the variable � are affected by noise with bounded intensity. Let �� = [�� , 0]′ be a noise vector. Substituting �� = � + �� for the measurement expression in (33) leads to �˙ � = −˜ �′� �� �˜� + 2˜ �′� (�� − �� ℎ′ �� ). Using Hölder and Young’s inequalities, appropriate algebraic manipulations show that ∣�� ∣ 2 �˙ � ≤ −˜ ∥�� ∥ �′� [�� − (2 ∣�� (�� + �)∣ ˆ + �� �� ) �] �˜� + �� where the constant �� > 0 can be made arbitrarily large provided that �� − (2 ∣�� (�� + �)∣ ˆ + �� �� ) � > 0. Under this condition, a sufficiently small constant �� > 0 exists such that �� (�) ≤ �−�� � �� (0) + with Δ� = �� �� Δ� �� (39) 2 ∥�� ∥ > 0 and �� = sup ∣�� ∣ . Hence, we conclude that the observer is ISpS with respect �≥0 to bounded measurement noise. 5.2. Stability of observer-controller To analyse the stability of the controller with respect to bounded errors in the estimation of � we observe that the noise affecting variables � and � in (8) verifies the matching condition; that is, the uncertain terms enter the state equation (9) at the same point as the control input. Since the intensity of the noise is bounded and the variables � and � are also assumed to be bounded, the noisy effects in � can be regarded as additional bounded external disturbances. Hence, it is concluded from the previous analysis that the controller is ISpS with respect to bounded errors in the estimation of �. Consider now the cascade system constituted by the observer �� represented by (34) and the controller �Ω represented by (13), (16), (20) as follows: �˙ 1 = �Ω (�1 , �2 , �1 ), �2 = �2 (40) �˙ 2 = �� (�2 , �2 ), where �1 := �¯ represents the tracking error of the controlled system, �2 := �˜� is the estimation error of the observer, the input �1 := �� + Ω� represents the external disturbances plus the effect of unmodeled 18 dynamics affecting �Ω , and �2 := �� represents the bounded noise input to the observer. Both the �1 and �2 subsystems with (�2 , �1 ) and �2 , respectively, as inputs are ISpS. Hence, it is shown by application of the Generalized Small-Gain Theorem (Jiang et al. (1994)) that the cascade system is ISpS with respect to bounded external disturbances and bounded noise in the depth measurements used by the observer; see Appendix E. 6. Results of simulations and discussion The performance of the control algorithms developed is illustrated using the results of computer simulations. The initial conditions and other parameters which are common to all the simulations are presented in Table 6. The specific parameters of each simulated test are presented in Table 7. The reference depth profiles used in the simulations are shown in Fig. 6. In depth tracking simulations �� (�) is approximated by cubic-splines, thus ensuring that the reference trajectory is twice differentiable in time. 6.1. Simulations setup In the scenario used for simulations the towed vehicle is required to regulate the pitch angle and the yaw error to zero while tracking a desired depth. Three types of depth trajectories were used: a real bathymetric profile with a total length of approximately 800m, a sinusoidal reference, and a constant depth trajectory; see Fig. 6. In all trajectories the depressor is assumed to be deployed at an approximately constant depth with oscillations in position that are imposed by the motion of the towing vessel at the sea surface. In the simulations, the towing system starts from rest and reaches the nominal towing speed. The operation of the system is simulated with towing speeds varying from 3knts to 8knts, using pigtail lengths of 30m, 50m, and 100m. The actual towing velocity used in simulations has a bias of ±10% the nominal velocity. This is used to account for the difficulty of maintaining a specified towing speed of the support ship. The wave induced perturbations are modelled according to the JONSWAP wave spectrum considering a sea-state of 3 − 4; see Table 6. Notice that the spectral density function of the wave-driven perturbations as defined in Section 2.6.2 is also a function of the towing speed. To investigate the robustness of the controlled system under the effect of non-measured ocean currents we consider an additional scenario where an underwater current with a velocity of 1knt makes the towfish drift sideways and diverge from the desired track. The control system was simulated in this scenario with distinct system parameters and different towing velocities; see Table 7. 6.1.1. Systems’s parameters and constraints The initial estimates of the model parameters represented by Π0 are set to ±10% of the corresponding true values. The exceptions to this rule are indicated in Table 7. The control planes have a maximum deflection of ±30 deg and the actuators’ dynamics are approximated by a first order system with a time constant equal to 0.1s. 6.1.2. Measurement noise To test the robustness of the proposed adaptive controller with respect to sensor noise, the measurements of �, �, �, �, and � used in the simulations are affected by mutually independent additive 19 Gaussian white noise whose standard deviations are represented by the constants �� , �� , �� , �� , and �� , respectively; see the values of these constants in Table 6. We notice that, when operating close to the surface, depth measurements may be affected by pressure variations induced by sea waves. Although this problem has not been addressed in the study, we propose to filter these variations by fusing the depth sensor measurements with the altitude measurements obtained with a sonar altimeter. 6.2. Results and discussion 6.2.1. Depressor and towfish motions The simulations reproduce closely the motion behaviour of the depressor determined by Chapman (1982): when the wave induced forces become nearly periodic the locus of the depressor in the vertical plane approximates an ellipse with the length of the major axis proportional to the vertical displacement of the ship and the minor axis one order of magnitude smaller; see Fig. 7. The plot of depressor and towfish motions shown in Fig. 10 is representative of the system motion with other pigtail lengths and towing speeds. These results show that the amplitude of surge and sway motions is not attenuated by the pigtail but heave motion is significantly reduced. This is in accordance with the experimental results cited in Section 2.3. However, our model shows low amplitude, high-frequency oscillations of the towfish towing point that are not reported in the cited works (see e.g. Fig. 10 ). This may be explained by the higher frequency contents of the sea-wave driven oscillations that are assumed to be transmitted to the depressor without attenuation and also by the intrinsic towfish oscillations that can be observed in Fig. 8. This natural oscillation of the towfish is inherent to the configuration with a small positive buoyancy. 6.2.2. Performance of the non-controlled system The errors observed with the undisturbed, non-controlled system towed at constant depth are shown in Fig. 8. The towing speed is 4����; see the configuration of Test Num. 1 in Table 7. The errors obtained with the non-controlled system subjected to wave-driven disturbances with a towing speed of 4���� at constant depth are plotted in Fig. 9; see the configuration of Test Num. 2 in Table 7. The external forces applied to the towfish are derived from the relative motions of depressor and towfish that are plotted in Fig. 10. These simulations show that the heave velocity of the non-controlled system is approximately zero. Corroborating the results of Preston (1992), the results illustrated in Fig. 9 show that yaw motion of the towed vehicle is not reduced with the two-part tow method. 6.2.3. System performance in constant-depth tracking and attitude control The results presented in Figs. 11 to 15 illustrate the performance of the system subjected to the same external disturbances of the previous tests but under the control laws derived in Section 4.1. Fig. 13 presents the attitude of the vehicle in roll. These results show that the motion of the controlled vehicle is in accordance with our previous assumption of negligible motion in roll. The actuation of the control surfaces is depicted in Fig. 15. Notice that when towed at 4 knts the control surfaces’ deflections are 20 far from the saturation value. Fig. 14 shows that the estimation error achieved by the observer of � is very small and remains inside the region of attraction established in Appendix E. This error decreases significantly with increasing towing speeds. 6.2.4. Bottom following performance The performance of the simulated system tracking the real bathymetric profile shown in Fig. 6 is illustrated in Figs. 16 to 17. A plot of the roll angle is presented in Fig. 17. Noting that bottom following is the most demanding scenario, we observe that by towing the vehicle at the velocity of 4���� the depth and pitch errors verify ∣¯ �� ∣ ≤ 0.3�, ∣¯ �� ∣ < 1 deg, the 2� interval of the yaw error corresponds to ∣¯ �� ∣ < 0.9 deg, and the roll angle verifies ∣�∣ < 0.25 deg . Notice that the tracking errors obtained in bottom-following missions increase considerably with increasing towing speed; compare the results of Test Num. 13 and 14 plotted in Figs. 16 and 20, respectively. Hence, for this type of mission the recommended maximum towing speed is 4knts. 6.2.5. Impact of pigtail length and towing speed on system performance The impact of the length of the secondary cable on the tracking errors obtained with the controlled system is summarized by the plot of the RMS errors shown in Fig. 21. The depth tracking error decreases slightly with the increase of the pigtail length. For practical purposes, however, the impact of the latter is not significant. To assess the impact of the pigtail length when the main towing forces are not aligned with the vehicle longitudinal axis we simulated the system using the sinusoidal trajectory represented in Fig. 6 at a towing speed of 3 knts (tests Num. 15 and 16). The errors obtained are similar to those obtained in the other simulations at comparable speeds. From these results we conclude that with this towing model the depth and pitch tracking errors of the controlled system are not practically affected by pigtail length. However, there is a significant impact on surge speed and lateral motion of the towfish, cf. Figs. 23 and 24. As a consequence, to mitigate accelerations in surge or lateral oscillations of the towfish position a longer pigtail is recommended. The impact of different towing speeds on the performance of the controlled system is summarized in Fig. 22. These results indicate that for constant depth-tracking the depth and attitude errors increase slightly with increasing towing speed. This effect becomes more pronounced for speeds above 6knts but 21 even at this speed the tracking errors remain inside the limits specified for the system. It is observed in simulations that the error in yaw is the most variable both with towing speed and pigtail length although its RMS value remains smaller than 1 deg. For critical applications it may be convenient to use lower towing speeds and larger pigtail lengths. It is also important to notice however, that at lower towing speeds the system is more susceptible to generate impulsive forces in the pigtail that can affect the stability of the system. This effect is illustrated in Fig. 25. 6.2.6. Adaptive controller The importance of adaptive control can be assessed based on a comparison of the tracking errors obtained with and without parameter adaptation, as shown in Fig 26 (a) and (b). The plots correspond to Tests Num. 11 and 12, respectively. These results show that adaptive control improves considerably the performance of the controller, especially in terms of depth tracking, when the error in the initial estimate of the parameters is large. It is shown that adaptive control may be fundamental to bound the tracking errors according to the system’s specifications. Parameter estimation implemented in the adaptive controller is illustrated in Fig. 27 for parameters �1 , �2 , and �3 . 6.2.7. Performance in the presence of ocean currents To analyse the performance of the controlled system in the presence of ocean currents several simulations were done including the effect of a current with a velocity of 1knt that causes lateral drift of the towfish. The behaviour of the system in this scenario is illustrated in Figs. 28 to 31. Due to the geometry of the towing arrangement the external forces transmitted by the pigtail tend to affect directly the orientation of the vehicle as shown in the plots of Fig. 28. The control effort required in this scenario is shown in Fig. 29; notice that the rudder deflection becomes close to the hard limit of −30deg. The tracking errors achieved by the system are shown in Fig. 30. Comparison of these errors with those achieved in the previous tests (see e.g. Fig. 11) shows that depth and pitch errors are not affected by the presence of the ocean current. In this test the amplitude of the yaw oscillations does not increase but the final orientation error has a bias of −1deg. The motion in roll remains negligible. The results obtained in simulations with a pigtail of 50m show that although the time response of the system varies with pigtail length, the amplitude of the tracking errors is equivalent to that obtained with a larger cable; see Fig. 31. The simulations executed with towing speeds of 8 knots, 10 knts, and 12 knts show that for this range of velocities the amplitude of the error caused by the ocean current does not vary with towing speed. 7. Conclusions and future work The paper addressed the problem of controlling an underwater towed vehicle in the vertical and horizontal planes. The system analysed consisted a two-stage towing arrangement that includes a long primary cable, a gravitic depressor, and a secondary cable. Although the paper did not address modelling the primary cable, the simulations used realistic wave models to drive the motion of the depressor-pigtail subsystem that transmits the wave driven disturbances to the towfish. 22 A nonlinear adaptive Lyapunov-based controller was designed and its performance assessed in simulation. Based on the simulation results we concluded that the nonlinear controller proposed is adequate for precise depth control and precise control of the attitude of a towed vehicle. The adaptive controller proved to be robust against vehicle parameter uncertainty and bounded external disturbances. The controlled system exhibits good performance at different equilibrium conditions which are dictated, among other factors, by the length of the secondary cable and the towing speed. The robustness of the adaptive controller was tested in the presence of sensor noise. To simplify practical implementations of the system, no direct measurements are required of surge, sway, and heave speeds. It was shown that the performance of the system is not affected by assuming a constant surge velocity equal to the nominal towing speed. A nonlinear observer was derived and proved to be a stable estimator of the heave velocity even in the presence of depth measurement noise. Although modelling and analysis of the complete towing system was beyond the scope of this work, some concluding remarks on this subject were drawn from the simulations. A pigtail 100m long is recommended to mitigate accelerations in surge and lateral oscillations of the towfish position. For towing speeds varying from 3knts to 8knts and constant depth tracking, the tracking errors remain inside the specified margin of error. For bottom following missions, unless the proposed specifications can be relaxed, the maximum towing speed recommended is 4knts. The results of simulations showed that the tracking errors achieved by the controlled system comply with the specifications imposed by the applications that motivated the system design. With appropriate choices of the pigtail length and towing speed, the magnitude of the depth error is made smaller than 30cm while pitch and yaw error magnitudes can be made smaller than 1 deg. The absolute value of the roll angle is smaller than 0.25 deg. These conclusions apply to the vehicle model adopted. However, the methodology developed in the paper affords the designer a powerful tool for combined plant-controller design of a large class of towed vehicles. The paper did not address the problem of control of the lateral position of the towfish. It was shown however, that in the absence of oceanic currents the lateral motion of the towfish can be kept one order of magnitude smaller than the corresponding displacement of the towing vessel. Even in the presence of underwater currents that cause the vehicle to drift laterally from the desired track, pitch and depth errors are of the same order of magnitude as those observed in the absence of currents. In this scenario, the yaw error becomes slightly biased and orientation control imposes a significantly larger actuation of the rudder. For applications that require a precise control of lateral position we propose a fully actuated system equipped with two vertical control surfaces. This will be the subject of future research. We intend to apply in the near future the techniques presented in this study to the control of an actual towfish used in marine geophysical surveying. Acknowledgements This research was supported in part by the FREESUBNET RTN of the CEC, projects DENO/FCTPT (PTDC/EEA-ACR/67020/2006), NAV/FCT-PT (PTDC/EEA-ACR/65996/2006), and EU CO3AUVs, and the FCT-ISR/IST plurianual funding program through the POS C Program that includes FEDER 23 funds. The work of of the first author benefited from a post-doc grant from the Portuguese Foundation for Science and Technology. 24 Appendix A. Error specifications The total field magnetic anomaly �� generated by a dipolar source of dipolar moment �� at an altitude � from the magnetized body is given by (see e.g. Telford et al. (1998)) �� = �� . �3 As a consequence, the error associated to a small variation Δ� in the altitude of measurement is �� − ��+Δ� = [ ] 3 �� (� + Δ�) − � 3 3 � 3 (� + Δ�) where the units of � , �� , and � are respectively, nano-Tesla (�� ), ��2 , and �. For a dipolar source, the vertical gradient is given by (see e.g. Breiner (1973))  ∂�  3� =− , ∂� � � and for small variations of � the error incurred by measuring at altitude � + Δ� is approximated by   ∂�  Δ� ∂�  3� − =− . ∂� � ∂� �+Δ� � (� + Δ�) Consider a typical scenario consisting of a magnetic body located on the sea floor with a dipolar moment �� = 104 ��2 , which generates a magnetic anomaly relative to the Earth’s ambient field with amplitude 10�� at an altitude of 10�. Elementary computations using the expressions above show that to achieve a maximum error of 1�� in measurements of the total field anomaly and 0.1�� /� in gradient measurements at altitude 10�, the maximum error in the altitude of the magnetic sensors must be approximately 0.3m. Appendix B. 6 DOF Equations of motion The system inertia matrix structure corresponds to a vehicle with a �� plane of symmetry and is defined as follows: � := ��� + �� = ⎡ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣ � ¯ − ��˙ 0 −��˙ 0 −��˙ 0 � ¯ − ��˙ 0 −��˙ 0 −��˙ 0 � ¯ − ��˙ 0 −��˙ 0 −��˙ 0 ��� − ��˙ 0 −��˙ 0 −��˙ 0 ��� − ��˙ 0 −��˙ 0 −��� − ��˙ 0 0 ⎤ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ 0 ⎥ ⎥ −��� − ��˙ ⎥ ⎥ ⎥ ⎥ 0 ⎦ ��� − ��˙ −��˙ where ��� is the rigid-body system inertia matrix and �� is the hydrodynamic system inertia matrix. 25 The Coriolis and centripetal matrix is: �(�) := ��� (�) + �� (�) = ⎡ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣ 0 −�� ¯ �� ¯ 0 −��˙ � ��˙ � �� ¯ 0 −�� ¯ ��˙ � 0 −��˙ � −�� ¯ �� ¯ 0 −��˙ � ��˙ � 0 0 −��˙ � ��˙ � 0 (��� − ��˙ )� ��˙ � 0 −��˙ � −��˙ � ��˙ � 0 −��� � ��� �− 0 −(��� − ��˙ )� (��� − ��˙ )� −��� � (��� − ��˙ )� ��� �− (��� − ��˙ )� 0 (��� − ��˙ )� ⎤ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎦ where ��� is the rigid-body Coriolis and centripetal matrix and �� is the hydrodynamic Coriolis and centripetal inertia matrix. The transformation matrix from the body-fixed frame {�} to the inertial coordinate frame {�} is defined as ⎡ �(�) := ⎣ �1 03×3 03×3 �2 ⎤ ⎦ with ⎡ ���� ⎢ ⎢ �1 := ⎢ ���� ⎣ −�� ⎡ 1 ⎢ ⎢ �2 := ⎢ 0 ⎣ 0 −���� + ������ ���� + ������ ���� ���� �� ��/�� ���� ���� + ������ ⎤ ⎥ ⎥ −���� + ������ ⎥ , ⎦ ���� ⎤ ⎥ ⎥ −�� ⎥ ⎦ ��/�� � where �(�) also denoted �� is the rotation matrix from the body-fixed frame to the inertial reference frame. Using the coordinates (�� , �� , �� ) of the center of buoyancy of the vehicle, the vector of restoring forces and moments is defined by 26 ⎡ ¯ − �)�� ¯ (� ⎢ ⎢ ¯ − �)���� ¯ ⎢ −(� ⎢ ⎢ ¯ − �)���� ¯ ⎢ −(� �(�) := ⎢ ⎢ ¯ ⎢ −�� ����� ⎢ ⎢ ¯ ¯ − �� ����� ⎢ −�� ��� ⎣ �� ���� ⎤ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥, ⎥ ⎥ ⎥ ⎥ ⎥ ⎦ where (�� , �� , �� ) are the coordinates of the center of buoyancy. The total hydrodynamic damping matrix is ⎡ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ �(�) := − ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣ 1 ¯ 2 �� 2 3 1 ¯ 2 �� 2 3 1 ¯ 2 �� 2 3 �� (�, �)� 1 ¯ 2 �� 2 3 �� (�, �)� 1 ¯ 2 �� 2 3 �� (�, �)� 1 ¯ 2 �� 2 3 �� (�, �)� 1 ¯ 2 �� 2 3 �� (�, �)� �� (�, �)� 1 ¯ 2 �� 2 3 �� (�, �)� �� (�, �)� 1 ¯ 2 �� 2 3 �� (�, �)� 1 ¯ 2 �� �� (�, �)� 1 ¯ 2 �� �� (�, �)� 1 ¯ 2 �� �� (�, �)� 1 ¯ 2 �� �� (�, �)� 1 ¯ 2 �� �� (�, �)� 1 ¯ 2 �� �� (�, �)� 1 ¯ 2 �� �� (�, �)� 1 ¯ 2 �� �� (�, �)� 1 ¯ 2 �� �� (�, �)� 0 0 0 ⎤ ⎥ ⎥ �∣�∣� ∣�∣ ⎥ ⎥ ⎥ ⎥ 0 �∣�∣� ∣�∣ 0 ⎥. ⎥ ⎥ �∣�∣� ∣�∣ 0 0 ⎥ ⎥ ⎥ 0 �∣�∣� ∣�∣ 0 ⎦ 0 0 �∣�∣� ∣�∣ 0 0 The forces and moments exerted by the control surfaces are given by ⎡ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ 1 2 � � := [¯ �� , �¯� , �¯� , � ¯� , � ¯� , � ¯� ] = �∥�1 ∥ �� ��� ⎢ ⎢ 2 ⎢ ⎢ ⎢ ⎢ ⎣ (�+�� )2 +(�+�� )2 +(�+�𝑠 )2 ��𝑠 �� � + �� 2� + �� + �� 0 −�1� (�� − �� ) �1� (� + �� ) ⎤ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥, ⎥ ⎥ ⎥ ⎥ ⎥ ⎦ where �� is the fin aspect ratio, �� is the span efficiency factor, and ��� represents the �-coordinate of the hydrodynamic center of the ��ℎ fin expressed in the body-fixed frame. The vector of external forces and moments expressed in the body-fixed referential includes the effects of the towing forces and moments and the external disturbances, as follows: �� := [�� , �� , �� , �� , �� , �� ]� . 27 Appendix C. Model of forces and moments applied to the towfish The vector �� = [�� , �� , �� ]′ that describes the position of the depressor expressed in {�} is determined by the model of the depressor motion described in Section 2.6. The model of the forces exerted at the pigtail adapted from Schuch (2004) is as follows. The spring coefficient of the pigtail is �� , �¯ = �� where �� is the elastic modulus of the cable and �� is the cable length. The distance between the depressor and the tow-point at the fish nose is ⎡ ⎛ �� /2 ⎢ ⎜ ⎜ � ⎢ Δ� := �� − ⎜�1 − �� ⎢ ⎣ ⎝ ⎤⎞ ⎥⎟ ⎥⎟ ⎥⎟ ⎦⎠ 0 0 and the difference between the velocity of the depressor and the tow-point at the fish nose is ⎛ ⎡ �� /2 ⎜ ⎢ ⎢ � ⎜ Δv := �˙ � − �� ⎜ �1 + �2 × ⎢ ⎝ ⎣ 0 0 ⎤⎞ ⎥⎟ ⎥⎟ ⎥⎟ . ⎦⎠ As a consequence, the force applied in the pigtail is ⎡ ⎢ ⎢ �� := ⎢ ⎣ ) ⎤ ( Δ� �¯ (∣Δ�∣ − �� ) + ¯� Δv′ ∣Δ�∣ ⎥ ⎥ ⎥, 0 ⎦ 0 √ �¯� ¯ � is the damping coefficient, with 𝜁 denoting the damping ratio and � ¯ � := � ¯ − ��˙ . ( ) Δ� Notice that �� results in a tension force developed in the pigtail iff �¯ (∣Δ�∣ − �� ) + ¯� Δv′ ∣Δ�∣ > 0; where ¯� := 2𝜁 otherwise the force applied to the towfish is null. The natural frequency of the pigtail subsystem is given by �� = √ 28 �¯ . � ¯� To enter the towfish dynamics, the force �� is transformed sequentially from the cable coordinate frame {�} to frame {�} and from this to frame {�}. The transformation matrix from {�} to {�} is given by the rotation matrix ⎡ where the angles �� := sin−1 Δ� =: �� − �. ( ��� ��� ⎢ ⎢ � := ⎢ ��� ��� �� ⎣ −��� Δ� �� ) and �� := sin−1 −��� ��� 0 ( Δ� �� ) ��� ��� ⎤ ⎥ ⎥ ��� ��� ⎥ , ⎦ ��� are illustrated in Fig. 3 with Δ� := �� − � and Appendix D. Inclusion of coupling terms in system dynamics Appendix D.1. Simplified model with coupling terms The inclusion of the coupling terms that represent the effects of motion in roll in equations (3), (4) and (5) leads to (� ¯ − ��˙ )�˙ − ��˙ �˙ − ��˙ �˙ − (� ¯ − ��˙ )�� −�∣�∣� ∣�∣� + �� (�1 , �, �) + (� ¯ − ��˙ )�� + �� (�, �) = (D.1) �¯� (�, �, �) + �� , (��� − ��˙ )�˙ − ��˙ �˙ − ��˙ �˙ + (��˙ − ��˙ )�� + ��� �2 −(��� − ��˙ )�� + (��� − ��˙ )�� − ��� �2 − �∣�∣� ∣�∣� + �� (�1 , �, �) + �� (�) = (D.2) � ¯� (�, �) + �� , (��� − ��˙ )�˙ − ��˙ �˙ + (��˙ − ��˙ )�� − ��� �2 +(��� − ��˙ − ��� + ��˙ )�� − (��� − ��˙ )�˙ + ��� �� − �∣�∣� ∣�∣� + �� (�1 , �, �) = (D.3) � ¯� (�, �, �) + �� . Notice that in equation (D.3) there are two terms than have been neglected in the expressions of the dynamics used by the controller. The term ��˙ �˙ was eliminated because ��˙ is off-diagonal; ��� �˙ is neglected because ��� is small and �˙ is assumed to be negligible. Appendix D.2. Derivation of equation (29) Equation (29) is derived as follows: 29 [ ]2 ¯ − �) ¯ cos �(cos � − 1) − (� ¯ − ��˙ )�� ∥Ω� ∥2 = �21 (� [ ] ¯ − �) ¯ cos �(cos � − 1) − (� ¯ − ��˙ )�� +2��1�1 (� [ ] 2 4 +2�22 (��� − ��� + ��˙ − ��˙ )2 �2 �2 + ��� � − 2(��� − ��� + ��˙ − ��˙ )��� �3 � [ 2 4 � +2�23 (��� − ��� + ��˙ − ��˙ )2 �2 � 2 + ��� ] +2(��� − ��� + ��˙ − ��˙ )��� �3 � 2 2 2 ) + ��3 + ��2 +2(��1   ¯ −� ¯ ¯ − �) ¯ 2 + 2 ∣� ≤ �21 (� ¯ − ��˙ )�2� �2� + (� ¯ − ��˙ ∣ �� �� � )  ( ¯ −� ¯ ¯ − ��˙ ∣ �� �� + � +(�� + �� )2 + 2(�� + �� )�1 ∣� [ ] 2 4 +2 �1 ∥�2 ∥2 + �22 ��� �� + �1 + 21 ∥�2 ∥2 [ ] 2 2 2 2 4 +2 �2 ∥�2 ∥ + �23 ��� �� + �2 + 12 ∥�2 ∥ + 2 ∥�2 ∥ 2 = �� ∥�2 ∥ + Δ� where �� := 2(�1 + �2 + 2) with �1 := �22 (��� − ��� + ��˙ − ��˙ )2 �2� , 2 6 �1 := 2�22 (��� − ��� + ��˙ − ��˙ )2 ��� �� , �2 := (��� − ��� + ��˙ − ��˙ )2 �2� , 2 6 �2 := 2�23 (��� − ��� + ��˙ − ��˙ )2 ��� �� , and Δ� is a positive constant defined as the sum of remaining terms in the right-hand side of the inequality. In the previous derivation we assumed that the sway velocity and the roll rate are bounded, and applied inequalities of the form �2 ≤ �2� and �� ≤ �� �� , where �� := sup ∣�∣ and �� := sup ∣�∣. We also used the �≥0 �≥0 ( ) inequality �� = � tan � cos1 � − 1 − � tan � ≤ �� �� + �� �� which is valid for all ∣�∣ , ∣�∣ ≤ �2 with �� and �� real, non-negative constants. We notice that for small values of � and �, �� ≪ �� + �� . Appendix D.3. Derivation of equation (32) From equations (18) and (31) we obtain �� 2 �˙ 3 ≤ −��3 + ∥�2 ∥ + ∇. 2�� Since �3 is a function of �2 , it follows that �3 (�) ≤ � −�� �3 (0) + � −�� �� 2�� ∫� 0 2 ∥�2 (� )∥ �−�� �� + Defining �� (�2 ) := � −�� �� 2�� ∫� 0 30 ∥�2 (� )∥2 �−�� ��, ∇ . � the derivative of �� (⋅) w.r.t. �2 , for a given � > 0 is ∂�� �� = �−�� ∂�2 �� Since �� (0) = 0 and ∂�� ∂�2 ∫� �2 (� ) �−�� ��. 0 > 0 in the domain �2 = [0, ∞), by definition �� (⋅) is a class K function. Appendix E. Nonlinear observer of � It is assumed that the surge and heave velocities verify � = �0 ≥ 1.5�/� and 0 ≤ � ≤ 1�/�, respectively. Under these conditions we can use tan−1 (� ) � (�2 + �2 ) ≈ (�0 + 0.3)�. This approximation improves with increasing � and can be used to simplify the state equation of the observer by linearising the nonlinear terms on �. Appendix E.1. Derivation of equation (37) Take �˙ � = −˜ �′� �� � ˜� + 2˜ �′� �� = −˜ �′� �� � ˜� + 2�� �(� ˜ 2−� ˆ 2 ). Using the equality �2 − � ˆ2 = �(� ˜ + �) ˆ we obtain �0 ∥2 ∣�� (�� + �)∣ ˆ �˙ � = −˜ ˜� + 2�� � ˜2 (� + �) ˆ ≤ −˜ �′� �� �˜� + 2 ∥˜ �′� �� � and therefore �˙ � ≤ −˜ �′� (�� − 2∣�� (�� + �)∣�)˜ ˆ �� . To make (�� − 2∣�� (�� + �)∣�) ˆ positive definite the following condition must hold: ⎡ ⎣ ˆ 2�1 − 2 ∣�� (�� + �)∣ �2 − cos � �2 − cos � −2(�� + ∣�� (�� + �)∣) ˆ ⎤ ⎦>0 This condition is verified if �2 ≥ 1, �1 > ∣�� (�� + �)∣ ˆ and ∣�� ∣ > ∣�� (�� + �)∣ ˆ because �� < 0. Given the bound �� , it is straightforward to show that a sufficient condition for the last inequality to hold is ∣�∣ ˜ < ∣�� ∣ − 21 ��� ��� ����� �� , 1 ��� 2 ��� ��� �� where ����� = ���(∣�� ∣) is the hard limit of the common deflection �� = �� + �� . Considering the current system’s parameters (see Tables 5 and 6), the previous condition establishes the region of attraction of the origin of � ˜0 as ∣�∣ ˜ < 1.24�/�. 31 Taking in account the bounds on the velocities assumed for the system, this region of attraction is sufficient to ensure convergence of the observer. Notice also that a larger constant �� improves the convergence of the observer. A larger value of �� can be achieved by increasing the nominal surge speed �0 or by increasing the system parameters ���� or �� . This is as expected since larger surge speeds and larger ���� and �� parameters increase the hydrodynamic efficiency of the control surfaces. Appendix E.2. Stability of the cascade observer-controller system Given the smooth functions �Ω (.) and �� (.) it is easily observed that (40) is a simplified version of the general interconnected system to which the Generalized Small-Gain Theorem applies; see Jiang et al. (1994). Since the �2 -subsystem is ISpS, then with �2 as output it has the unboundedness observability (UO) property. The same property applies to the �1 -subsystem. The results of stability analysis in Sections 4.3 and 5.1 are used in the following demonstration. The inequality [ 2 2 2 ∥¯ �∥ ≤ 12 �−�� ∥¯ � (0)∥ + �(0)∥ + ∥¯ 2 �−�� ∥¯ �(0)∥ + �� (∥�2 ∥) + ∇ � + 1 � 1 2 ] 2 ∥Π0 ∥� + �� (∥�2 ∥) + 2 ∥¯ � (0)∥ + 1 2� 2 ∇ � ≤ ∥Π0 ∥� follows from (32). From (39) it can be concluded that �˜′� � � ˜� (0) + ˜� ≤ �−�� � � ˜′� (0)� � Δ� Δ� 2 ≤ 132.14�−��� ∥˜ �� (0)∥ + . �� �� Recall the definitions of �1 := �¯, �2 := �˜� and introduce the functions 2 �1 (∥�1 (� )∥ , �) := �−�� ∥�1 (� )∥ , 2 �2 (∥�2 (� )∥ , �) := �−�� � ∥�2 (� )∥ , �1� (.) := �� (∥�2 ∥) , �1� (.) := 0, �1 := �2 := 1 ∇ � + 2 Δ0 �0 , ∥¯ � (0)∥2 + 1 2� ∥Π0 ∥2� , where �� are class KL functions, �1� is a function of class K, called the (nonlinear) gain from input to output, and �� are non-negative constants (for � = 1, 2). It is immediately verified that ∥�1 (�)∥ ≤ �1 (∥�1 (0)∥ , �) + �1� (∥�1 ∥) + �1 , ∥�2 (�)∥ ≤ �2 (∥�2 (0)∥ , �) + �2 . The remaining conditions of the Generalized Small-Gain Theorem are trivially verified since �1� (.) := 0. 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Dynamics and control of a towed vehicle in transient mode. International Journal of Offshore and Polar Engineering 10 (1), pp.19–25. Zumberge, M. A., Ridgway, J. R., Hildebrand, J. A., 1997. A towed marine gravity meter for near-bottom surveys. Geophysics 62 (5), 1386–1393. 35 Figure 1: Two-part towing arrangement. 36 Figure 2: Physical dimensions of the towfish. The fins and control surfaces are grouped in pairs and numbered according to this figure. 37 Figure 3: External forces applied to the vehicle towing-point. Notice the orientation of the reference frames {�} and {�} relative to the inertial frame {�} in which the distance vectors Δ� and Δ�, and the towing forces ��� ,��� , and ��� are expressed. 38 Figure 4: Definition of encountering angle �𝑒 and respective wave-aspects. 39 JONSWAP spectrum (w0=0.4 rad/s, Hs=1.5m) 1.4 1.2 S(ω) (m2 s) 1 0.8 0.6 0.4 0.2 0 0 0.5 1 1.5 ω (rad/s) Figure 5: JONSWAP wave spectrum used in the simulations. The specific wave spectrum used in each simulation is also a function of the towing speed 40 Reference depth profiles −15 −20 depth (m) −25 −30 bottom profile −35 synthetic profile constant depth −40 0 100 200 300 400 500 600 700 800 distance (m) Figure 6: Reference depth profiles used in simulations. Depths are represented by negative values to facilitate the visualization of the depth profile. 41 Locus of depressor Locus of depressor in plane xz 32 32 31.5 31.5 31 31 30.5 30 z (m) z (m) 30.5 29.5 29 30 29.5 28.5 29 28 0 28.5 −5 y (m) −10 −0.4 0.2 0 −0.2 x (m) 0.4 28 −0.5 0 x (m) 0.5 Figure 7: Locus of the gravitic depressor corresponding to a nearly periodic motion of the towing vessel with lateral motion and zero surge speed. 42 Tracking errors: depth(z), pitch(θ), and yaw(ψ) 0.1 zerr (m) 0.05 0 −0.05 −0.1 0 100 200 300 400 500 600 0 100 200 300 400 500 600 0 100 200 300 time (s) 400 500 600 θerr(deg) 2 1 0 −1 −2 ψerr(deg) 1 0.5 0 −0.5 −1 Figure 8: Response of the undisturbed non-controlled system, towed at 4���𝑠 (Test Num.1). 43 Tracking errors 3 zerr (m) 2 1 0 −1 −2 −3 0 100 200 300 400 500 600 0 100 200 300 400 500 600 0 100 200 300 time (s) 400 500 600 θerr(deg) 10 5 0 −5 −10 ψerr(deg) 100 50 0 −50 −100 Figure 9: Tracking errors obtained with the disturbed, non-controlled system towed at 4knts; constant depth (Test Num.2). Compare these results with those obtained with the controlled system, in Fig. 11. 44 Relative positions of towfish nose and depressor towfish depressor 102 xnose (m) 0.5 0 101 −0.5 −1 100 −1.5 −2 0 100 200 300 400 500 99 600 0 100 200 300 400 500 600 0 100 200 300 time (s) 400 500 600 xdepressor (m) 1 20 y (m) 10 0 −10 −20 z (m) 35 30 25 Figure 10: Relative positions of depressor and towfish nose for the non-controlled system towed at 4knts; constant depth (Test Num.2). 45 Tracking errors 0.2 zerr (m) 0.1 0 −0.1 −0.2 0 100 200 300 400 500 600 0 100 200 300 400 500 600 0 100 200 300 time (s) 400 500 600 1 θerr(deg) 0.5 0 −0.5 −1 ψerr(deg) 2 1 0 −1 −2 Figure 11: Tracking errors obtained with the controlled system towed at 4knts; constant depth trajectory (Test Num.6). Compare these results with those obtained with the non-controlled system, in Fig. 9 46 0.25 2 1.5 0 200 400 0 −0.25 −0.5 600 0.2 5 0.1 2.5 0 −0.1 −0.2 0 200 400 3 0.2 1.5 −0.2 −0.4 0 200 400 600 time (s) 200 400 600 0 200 400 600 0 200 400 600 0 0.4 0 0 −2.5 −5 600 r (deg/sec) v (m/sec) p (deg/sec) 2.5 1 w (m/sec) Angular rates 0.5 q (deg/sec) u (m/sec) Linear velocities 3 0 −1.5 −3 time (s) Figure 12: Linear and angular velocities of the controlled system towed at 4knts; constant depth trajectory (Test Num.6). 47 Roll angle and 2σ interval 0.3 0.2 φ (deg) 0.1 0 −0.1 −0.2 −0.3 0 100 200 300 time (s) 400 500 600 Figure 13: Roll angle of the controlled towfish at 4knts towing speed, constant depth trajectory (Test Num.6). 48 Observer estimation error of heave velocity (w) error (m/sec) 0.05 0 −0.05 0 100 200 300 time (sec) 400 500 600 Figure 14: Error in the estimation of heave velocity at 4knts towing speed, constant depth trajectory (Test Num.6). 49 stern elevator (deg) bow elevator (deg) Actuation of control surfaces 20 10 0 −10 −20 0 100 200 300 400 500 600 0 100 200 300 400 500 600 0 100 200 300 time (s) 400 500 600 20 10 0 −10 −20 rudder (deg) 20 10 0 −10 −20 Figure 15: Actuation of the control surfaces of the towfish at 4knts towing speed, constant depth trajectory. The upper and lower dashed lines represent the hard-limits of deflection (Test Num.6). 50 Tracking errors 0.4 zerr (m) 0.2 0 −0.2 −0.4 0 50 100 150 200 250 300 350 400 0 50 100 150 200 250 300 350 400 0 50 100 150 200 time (s) 250 300 350 400 1 θerr(deg) 0.5 0 −0.5 −1 ψerr(deg) 2 1 0 −1 −2 Figure 16: Tracking errors in a bottom-following mission at 4knts towing speed (Test Num.13). 51 Roll angle and 2σ interval 0.3 φ (deg) 0.2 0.1 0 −0.1 −0.2 −0.3 0 50 100 150 200 time (s) 250 300 350 400 Figure 17: Roll angle of the controlled towfish at 4knts towing speed in a bottom-following mission (Test Num.13). 52 0.25 2 1.5 0 100 200 300 0 −0.25 −0.5 400 0.2 5 0.1 2.5 0 −0.1 −0.2 0 100 200 300 3 0.5 1.5 −0.5 −1 0 100 200 time (s) 300 400 100 200 300 400 0 100 200 300 400 0 100 200 time (s) 300 400 0 1 0 0 −2.5 −5 400 r (deg/sec) v (m/sec) p (deg/sec) 2.5 1 w (m/sec) Angular rates 0.5 q (deg/sec) u (m/sec) Linear velocities 3 0 −1.5 −3 Figure 18: Linear and angular velocities of the towfish in a bottom-following mission at 4knts towing speed (Test Num.13). 53 stern elevator (deg) bow elevator (deg) Actuation of control surfaces 20 10 0 −10 −20 0 50 100 150 200 250 300 350 400 0 50 100 150 200 250 300 350 400 0 50 100 150 200 time (s) 250 300 350 400 20 10 0 −10 −20 rudder (deg) 20 10 0 −10 −20 Figure 19: Actuation of the control surfaces of the towfish in a bottom-following mission at 4knts towing speed. The upper and lower dashed lines represent the hard-limits of deflection (Test Num.13). 54 Tracking errors 1 zerr (m) 0.5 0 −0.5 −1 0 20 40 60 80 100 120 140 160 180 200 0 20 40 60 80 100 120 140 160 180 200 0 20 40 60 80 100 time (s) 120 140 160 180 200 3 θerr(deg) 2 1 0 −1 −2 −3 3 ψerr(deg) 2 1 0 −1 −2 −3 Figure 20: Tracking errors in a bottom-following mission at 8knts towing speed (Test Num.14). 55 RMS values of tracking errors Depth error (m) 0.05 0.045 0.04 20 30 40 50 60 70 80 90 100 110 Attitude error (deg) 1 θ (deg) ψ (deg) 0.8 0.6 0.4 0.2 20 30 40 50 60 70 pigtail length (m) 80 90 100 110 Figure 21: Impact of different pigtail lengths on system performance. Results obtained with a constant depth trajectory and towing speed of 6knts (Tests Num. 7, 9, and 10). 56 RMS values of tracking errors Depth error (m) 0.07 0.06 0.05 0.04 0.03 2 3 4 5 6 7 8 4 5 towing speed (knts) 6 7 8 0.8 θ (deg) ψ (deg) Attitude error (deg) 0.7 0.6 0.5 0.4 0.3 0.2 0.1 2 3 Figure 22: Impact of different towing speeds on system performance. Results obtained with a constant depth trajectory and a pigtail length of 100m (Tests Num. 5 to 8). 57 Linear velocities (pigtail length=30m) 4 3 3 2 1 0 1 200 400 600 0.2 0.2 0.1 0.1 0 −0.1 −0.2 0 200 400 200 400 600 0 200 400 600 0 200 400 600 0 w (m/sec) 0.5 0 −0.5 0 −0.1 −0.2 600 0.5 w (m/sec) 2 0 0 v (m/sec) v (m/sec) u (m/sec) u (m/sec) Linear velocities (pigtail length=100m) 4 0 200 400 0 −0.5 600 time (s) time (s) (a) (b) Figure 23: Impact of different pigtail lengths on linear velocities: (a) pigtail length=100m; (b) pigtail length=30m (Tests Num.15 and 16, respectively). 58 Lateral motion of towfish towing point with different pigtail lenghts y (m) 0.5 0 (a) −0.5 −1 0 100 200 300 400 500 600 y (m) 0.5 0 (b) −0.5 −1 0 100 200 300 400 500 600 Figure 24: Impact of different pigtail lengths on lateral motion of the towfish: (a) pigtail length=100m; (b) pigtail length=30m (Tests Num.15 and 16, respectively). 59 Distance between depressor and towfish 30.5 distance (m) 30 29.5 29 distance depressor−towfish nominal cable length 28.5 28 27.5 27 0 100 200 300 400 500 600 400 500 600 Pigtail cable tension 1200 tension (N) 1000 800 600 400 200 0 0 100 200 300 time (s) Figure 25: Distance between depressor and towfish nose and cable tension at 3knts towing speed (Test Num.16). Notice the impulsive forces that are developed when the secondary cable is suddenly stretched. 60 Tracking errors: without adpative control zerr (m) Tracking errors: with adaptive control 1 1 0.5 0.5 0 0 θerr(deg) −0.5 −0.5 0 400 600 0 3 2 2 1 1 0 0 −1 −1 −2 ψerr(deg) 200 3 0 200 400 −2 600 0 4 4 2 2 0 0 −2 −2 −4 0 200 400 600 time (s) −4 0 200 400 600 200 400 600 200 400 600 time (s) (a) (b) Figure 26: Impact of adaptive control on system performance; simulation using a sinusoidal trajectory with towing speed 6knts. (a) with adaptive control; (b) without adaptive control (Tests Num.11 and 12, respectively). 61 estimated Estimated vs. true system parameters true a−1 (Kg) 1 135 130 125 120 0 100 200 300 400 500 600 0 100 200 300 400 500 600 0 100 200 300 time (s) 400 500 600 a−1 (Kg m2) 2 460 440 420 400 380 a−1 (Kg m2) 3 440 420 400 380 Figure 27: Parameter estimation implemented in the adaptive controller (Test Num.5). 62 External forces in body frame External moments in body frame 1 0.5 1000 τφ (Nm) τu (N) 1500 500 0 0 100 200 300 −1 400 500 τθ (Nm) τv (N) −500 −1000 0 100 200 300 200 300 400 0 100 200 300 400 0 100 200 time (sec) 300 400 0 1000 τψ (Nm) 50 τw (N) 100 100 −100 400 100 0 −50 −100 0 200 0 −1500 0 −0.5 0 100 200 300 400 0 −1000 −2000 Figure 28: External forces and moments acting on the towfish at 8knts towing speed with towfish lateral drift in the presence of 1knt ocean current; pigtail length of 100m, constant depth trajectory (Test Num.17). 63 Actuation: common (δc), differential (δd), and rudder (δr) (angles in deg.) δc=δb + δs 10 5 0 −5 −10 0 50 100 150 200 250 300 350 400 0 50 100 150 200 250 300 350 400 0 50 100 150 200 time (s) 250 300 350 400 δd=δb − δs 1 0.5 0 −0.5 −1 10 δr 0 −10 −20 −30 Figure 29: Actuation of the control surfaces of the towfish with a pigtail of 100m and 8knts towing speed in the presence on 1knt ocean current, constant depth trajectory (Test Num.17). 64 Tracking errors 0.2 zerr (m) 0.1 0 −0.1 −0.2 0 50 100 150 200 250 300 350 400 0 50 100 150 200 250 300 350 400 0 50 100 150 200 time (s) 250 300 350 400 1 θerr(deg) 0.5 0 −0.5 ψerr(deg) −1 2 1 0 −1 −2 −3 −4 −5 Figure 30: Tracking errors achieved with a pigtail of 100m and 8knts towing speed in the presence on 1knt ocean current, constant depth trajectory (Test Num.17). Compare these results with those shown in Fig. 11 . 65 Tracking errors 0.2 zerr (m) 0.1 0 −0.1 −0.2 0 20 40 60 80 100 120 140 160 180 200 0 20 40 60 80 100 120 140 160 180 200 0 20 40 60 80 100 time (s) 120 140 160 180 200 1 θerr(deg) 0.5 0 −0.5 −1 ψerr(deg) 2 0 −2 −4 −6 Figure 31: Tracking errors achieved with a pigtail of 50m and 8knts towing speed in the presence on 1knt ocean current, constant depth trajectory (Test Num.20). 66 Table 1: System model notation {�} {�} {�} �� ¯� ¯ � �(⋅) ���� ��� ���� �� �� �� (⋅) ��� (⋅) �� (⋅), �� (⋅), �� (⋅) �� (⋅), �� (⋅), �� (⋅) �(⋅) �� �� (⋅) �(⋅) �(⋅) �1 (⋅) �2 (⋅) �¯ �� �� � ¯ � �� ��� �� �� , �� , �� � �� � �� �� ¯ � ��� Body-fixed reference frame. Secondary cable reference frame. Inertial reference frame. Aspect ratio of a control surfaces. Damping coefficient of pigtail. Buoyancy of towfish. Coriolis and centripetal matrix. Approximate gradient of the hull lift coefficient as a function of the angles � and �. Approximate gradient of the fins lift coefficient as a function of the angles � or �. Approximate gradient of the hull pitch damping coefficient as a function of the angles � and �. Approximate gradient of the hull yaw damping coefficient as a function of the angles � and �. Hydrodynamic Coriolis and centripetal matrix. Rigid-body Coriolis and centripetal matrix. Hydrodynamic damping coefficients of the hull moments associated with roll, pitch, and yaw. Hydrodynamic damping coefficients of the hull forces in surge, sway, and heave. Hydrodynamic damping matrix. Elastic modulus of pigtail. Dynamic pressure function. Vector of restoring forces and moments expressed in the body-fixed frame. Rotation matrix transforming the position and orientation vector from {�} to {�}. Rotation matrix transforming the position vector from {�} to {�}. Rotation matrix transforming the orientation vector from {�} to {�}. Spring constant of pigtail. Length of the body of the towfish. Length of the secondary cable (pigtail). Mass of the vehicle. System inertia matrix. Hydrodynamic inertia matrix. Rigid-body inertia matrix. Position of the depressor expressed in {�}. Normalized 3D components of the depressor excursion expressed in {�}. Rotation matrix equal to �1 (�2 ) . Rotation matrix from {�} to {�}. Planform area of the rudder and horizontal planes, tip to tip. Dry weight of towfish. �-coordinate of the hydrodynamic center of the ��ℎ fin expressed in the body-fixed frame. 67 Table 2: Controller and observer notation �1 ,�2 ,�3 �2 ,ˆ �3 � ˆ1 ,ˆ � �ˆ � �¯ �¯� , �¯� , �¯� �� (⋅) �� (⋅) �� (⋅) �� (⋅) �� �� (⋅) �� (⋅) �� (⋅) �� (⋅) ℎ � �� (⋅) �� � � � ¯ � � � � ˆ �� � ˆ� � ˜� �� �ˆ� � �� �1 = [�, �, �]′ �2 = [�, �, �] True values of system parameters used in matrices Γ1 ,Γ2 , and Γ3 . Estimated values of �1 ,�2 , and �3 . Matrix defined from system parameters �1 ,�2 ,�3 . Estimated value of matrix �. Vector representing the effect of external forces and moments in system dynamics, expressed in {�}. Tracking error vector. Components of the tracking error vector in depth, pitch, and yaw, respectively.. State independent observer component. Vehicle hydrodynamic forces associated with heave. Vehicle hydrodynamic forces associated with pitch. Vehicle hydrodynamic forces associated with yaw. Observer nonlinear dynamic matrix. State dependent observer component. Vehicle restoring force associated with heave. Vehicle restoring moment associated with pitch. Vehicle restoring moment associated with yaw. Measurement coupling matrix of the observer. Observer linear dynamic matrix. Nonlinear gain of the observer. Depth measurement noise. Pitch rate. Yaw rate. Control variable. Surge velocity. Sway velocity. Heave velocity. Heave velocity estimated by the observer. Observer state. Observer estimated state. Observer error. Measurements used by the observer. Observer estimated measurements . Depth of the vehicle. Desired depth. Pose of the vehicle expressed in {�} in the simplified motion model. Velocity of the vehicle relative to {�} in the simplified motion model. 68 Table 3: System model notation (Greek) � �1 �2 �� 𝜁 � �1 �2 � ��� ��� � �� , �� , �� , �� , �� , �� �� , �� , �� �0 �� Δ� Δ� Δ� Δ� Position and orientation vector expressed in {�}. Position of the origin of {�} expressed in {�}. Vector of Euler angles. Span efficiency factor of control surfaces. Pigtail damping ratio. Velocity vector expressed in the body-fixed frame. Vector of linear velocities of the vehicle expressed in {�}. Vector of angular velocity of the vehicle expressed in {�}. Density of water. Vector of external forces applied to the towfish expressed in {�}. Vector of external moments applied to the towfish expressed in {�}. Vector of forces and moments generated by the control surfaces expressed in frame {�}. Components of � . Phase delays of each of the 3� components of the periodic motion of the depressor. Modal frequency of the JONSWAP wave spectrum. Frequency of encounter. Distance between the depressor and the towing point in the towfish. Difference between the velocity of the depressor and the towfish velocity at the tow-point, expressed in {�}. � − ���� distance between the depressor and the towfish tow-point in referential {�}. � − ���� distance between the depressor and the towfish tow-point in referential {�}. 69 Table 4: Controller and observer notation (Greek) � � � �� , �� , �� �� � �1 , �2 , �3 ��1 , ��2 , ��3 �� � �� Γ1 , Γ2 , and Γ3 Γ�1 , Γ�2 , and Γ�3 Γ̂1 , Γ̂2 , and Γ̂3 Ω� Angle of attack. Angle of side-slip. Vector of deflection angles of control surfaces. Deflection angles of bow, stern and rudder control surfaces, respectively. Heave force used by the observer. Pitch angle of the vehicle. Functions of measured variables used in the linear parametric model. Additional functions corresponding to the vertical/horizontal coupling terms. Estimation error of the nonlinear component of observer. Yaw angle of the vehicle. Desired yaw. Matrices of system parameters in the linearly parametrized formulation. Additional matrices corresponding to the vertical/horizontal coupling terms. Estimated values of Γ1 ,Γ2 , and Γ3 . Vector representing the coupling components of the system dynamics due to motion in roll. 70 Table 5: Values of system parameters used in the simulations Parameter Added mass and inertia Moments and products of Inertia Hydrodyn.Damping Mass of the towfish Towfish dry weight Volume of hull Buoyancy force Length of the body Center of gravity (CG) Center of buoyancy (CB) Gradient of body lift coeff. Gradient of body pitch. coeff. Length of pigtail Elastic modulus of pigtail Pigtail damping ratio Gradient of fin lift coeff. Planform area of fins Fin Aspect Ratio Span efficiency factor Coordinates of fin num. 1 Density of water Symbol ��˙ ��˙ ��˙ ��˙ ��˙ ��˙ ��˙ ��˙ ��˙ ��˙ ��˙ ��˙ ��˙ ��˙ ��˙ ��˙ ��˙ ��˙ ��� ��� ��� ��� �∣�∣� �∣�∣� �∣�∣� �∣�∣� �∣�∣� � ¯ ¯ � �¯ ¯ � �� (�𝐺 , �𝐺 , �𝐺 ) (�� , �� , �� ) ���� ���� �� �� 𝜁 ��� �� �� �� (�1� , ��1 , ��1 ) � Value −8.7849 0 −18.64 0 0 0 −1.5 −1.1 −18.64 0 −2.0 −1.6 −1.6 −60 0 −260.55 −60 −260.55 17.4 174.49 174.49 1.74 −125 −500 −500 −10 −10 113.5 1112 1 1167.6 2.75 (0, 0, 0) (0, 0, −0.08) 0.3508 0.1308 100 or 50 5� + 4 1 5.24 1.36 6.8 0.97 (1, 0, 0) 1000 71 Units �� �� �� �� � �� � �� � �� � �� �� �� � �� � �� � �� � �� �2 �� �2 �� �2 �� �2 �� �2 �� �2 �� �2 �� �2 �� �2 �� �2 �� �2 �� �2 �� �2 �� �2 �� � �3 � � � � − − � � − − �2 − − � �� �−3 Table 6: General simulations’ parameters Type of Parameter Initial conditions Bottom following Synthetic profile Constant depth Initial velocities Initial angular rates Velocity of ocean current Velocity bounds Controller gains Observer gains (minima) Measurement noise std.dev. Symbol Value Units �1 �1 �1 [�, �, �]′ [�, �, �]′ �� �� �� �� �� �� m n [�1 , �2 ]′ �� �� �� �� �� [0, 0, 15]′ [0, 0, 22.5]′ [0, 0, 30]′ [0, 0, 0]′ [0, 0, 0]′ [−0.5, 0.25, 0]′ 1 0.2 0.5 ����(10, 5, 50) ����(5, 10, 10) 1.0� − 8 1.5� + 6 [10, 10]′ 0.1 0.1 1 0.5 0.5 m m m m/s rad/s m/s m/s m/s rad/s m deg deg deg/s deg/s Parameters of wave-driven disturbances: JONSWAP parameters: Significant wave height Modal frequency Max. frequency Num. spectral components �� �� �� �� �� �� 0.1 0.01 0.89 �/4 �/2 0 �� �� ���� � 1.5 0.4 1.5 100 72 rad rad rad m rad/s rad/s Table 7: Simulations’ parameters by Test Number Test Num. 1 2 3 4 5 6 7 8 9 10 11 12 Depth profile const. depth const. depth const. depth const. depth const. depth const. depth const. depth const. depth const. depth const. depth sinusoidal sinusoidal Towing speed 4 knts 4 knts 4 knts 4 knts 3 knts 4 knts 6 knts 8 knts 6 knts 6 knts 6 knts 6 knts Pigtail length 100m 100m 50m 30m 100m 100m 100m 100m 50m 30m 100m 100m 13 14 15 16 17 18 19 20 bottom following bottom following sinusoidal sinusoidal const. depth const. depth const. depth const. depth 4 knts 8 knts 3 knts 3 knts 8 knts 10 knts 12 knts 8 knts 100m 100m 100m 30m 100m 100m 100m 50m Other conditions Undisturbed; non-controlled. Non-controlled. Non-controlled. Non-controlled. Initial 100% estimation error of parameters �1 ,�2 , �3 . Initial 100% estimation error of parameters �1 ,�2 , �3 ; without adaptive control. Subjected to ocean current with a velocity of 1knt Subjected to ocean current with a velocity of 1knt Subjected to ocean current with a velocity of 1knt Subjected to ocean current with a velocity of 1knt 73