System Identification

This paper presents a process for the estimation of tire-road forces, vehicle sideslip angle and wheel cornering stiffness. The method uses measurements (yaw rate, longitudinal/lateral accelerations, steering angle and angular wheel velocities) only from sensors which can be integrated or have already been integrated in modern cars. The estimation process is based on two blocks in series: the first block contains a sliding-mode observer whose principal role is to calculate tire-road forces, while in the second block an extended Kalman filter estimates sideslip angle and cornering stiffness. More specifically, this study proposes an adaptive tire-force model that takes variations in road friction into account. The paper also presents a study of convergence for the sliding-mode observer. The estimation process was applied and compared to real experimental data, in particular wheel force measurements. Experimental results show the accuracy and potential of the estimation process.

Precision positioning systems are fundamental components in industrial machines such as machine tools, measuring machines and semiconductor manufacturing systems. The performance of the machines depends on the positioning systems. For precise positioning are often used DC motors. The paper deals with experimental method of DC motor friction identification. The aim of the paper is determining of suitable way of DC motor friction modeling. At first the basic static friction models are introduced and linear mathematical model of DC motor is established. DC motor friction identification is by means of experimental method done using Newtonś mechanics. For experiment PITTMAN 9413 motor with encoder is used. For measuring of friction torque the measuring I/O card is used which cooperates with Matlab / Simulink by Real Time Toolbox. Then the mathematical model is simulated and compared with real model of DC motor. In the conclusion the effects of particular friction models affecting DC motor are described and the best way of friction modeling for DC motor is determined.

Conventional mechanism (i.e. rigid link mechanisms) has limited accuracy and repeatability because of friction, backlash. Mechanism offers a relative motion between rigid links via joints. Conventionally these joints are pin, sliding, rolling, liquid film etc. These joints have inherent nonlinear frictional characteristics which are creating difficulty in control micro-nano positioning in precision mechanisms. To achieve high resolution and precision problems of friction, backlash in mechanism needs to be removed. These joints are replaced by flexible elements such as flexural hinge, flexural links which offers advantage of frictionless and smooth motion. Flexural mechanisms are developed using basic building blocks such as hinges and planar type. Flexural building blocks should be such that it offers lesser / zero resistance to motion in desired direction and maximum / infinite resistance in orthogonal direction. Motion generated in orthogonal direction is called parasitic motion. Double Flexural Manipulator (DFM) is a planar type building block which offers theoretically zero parasitic error motion with negligible amount of rotation of motion stage. This article presents design, development, system integration, system identification and PID control implementation on DFM. Developed flexural stage is integrated with desktop PC via dSPACE micro-controller. System identification (Static and Dynamic) is carried out and performance parameters such as stiffness, natural frequency, damping characteristics and frequency characterization of DFM are estimated and validated via due experimentations. Estimated transfer function of DFM is further used for development of control system, and PID control parameters are tuned. Real time PID implementation gives a position accuracy of few microns at high scanning speed of 2.5 mm/s.

We consider the tracking problem of unknown, robustly stabilizable, multi-input multi-output (MIMO), affine in the control, nonlinear systems with guaranteed prescribed performance. By prescribed performance we mean that the tracking error converges to a predefined arbitrarily small residual set, with convergence rate no less than a prespecified value, exhibiting maximum overshoot as well as undershoot less than some sufficiently small preassigned constants. Utilizing an output error transformation, we obtain a transformed system whose robust stabilization is proven necessary and sufficient to achieve prescribed performance guarantees for the output tracking error of the original system, provided that initially the transformed system is well defined. Consequently, a switching robust control Lyapunov function (RCLF)-based adaptive, state feedback controller is designed, to solve the stated problem. The proposed controller is continuous and successfully overcomes the problem of computing the control law when the approximation model becomes uncontrollable. Simulations illustrate the approach.

One of few limitations of remote laboratory technology is the fact that access and usability of such laboratories depend largely on the existence of favorable bandwidth conditions between the remote user and the system under test. This dependence is regrettable because some of the institutions likely to find remote laboratories attractive are also those most likely to have severe bandwidth limitations. Also, a typical remote laboratory will be completely unusable to remote students in the event of an outright network downtime. In this paper, we propose a hybrid online laboratory architecture that allows the automatic generation of accurate software models of remote laboratories. Such models can be hosted closer to the student and during periods of unfavorable bandwidth conditions, students can successfully interact with such models in lieu of the real hardware. We identify the challenges that need to be resolved for such a scheme to be useful and discuss the process by which suitabl...

This work presents system identification using neural network approaches for modelling a laboratory based twin rotor multi-input multi-output system (TRMS). Here we focus on a memetic algorithm based approach for training the multilayer perceptron neural network (NN) applied to nonlinear system identification. In the proposed system identification scheme, we have exploited three global search methods namely genetic algorithm (GA), Particle Swarm Optimization (PSO) and differential evolution (DE) which have been hybridized with the gradient descent method i.e. the back propagation (BP) algorithm to overcome the slow convergence of the evolving neural networks (EANN). The local search BP algorithm is used as an operator for GA, PSO and DE. These algorithms have been tested on a laboratory based TRMS for nonlinear system identification to prove their efficacy.

Neuro-fuzzy systems based on Radial Basis Function Networks (RBFN) and other hybrid artificial intelligence techniques are currently under intensive investigation. This paper presents a RBFN training algorithm based on evolutionary programming and cooperative evolution. The algorithm alternatively applies basis function adaptation and backpropagation training until a satisfactory error is achieved. The basis functions are adjusted through an error goal function obtained through training and testing of the second part of the network. The algorithm is tested on benchmark data sets. It is applicable to on-line adaptation of RBFN and building adaptive intelligent systems

In this paper a least mean-square (LMS) adaptive digital filter (ADF) is used in order to detect the extraordinary levels of air pollution data, to predict the future air pollution levels, and to identify the unknown parameters in the environmental system. The technique used here is based on the recursive adaptive digital filtering method proposed by White, which is an extension of the usual ADF by Widrow. For the 0, data developed at Sooka, Koshigaya, Kasukabe, and Kawaguchi, Japan, the extraordinary levels of the 0, data are detected by using the recursive ADF. For the SO2 data at Komatsushima, Japan, the predicted values of the SO2 levels are obtained by using the ADF as the predictor. Finally, a new identification method is proposed to fiid the unknown parameters of the AR, MA, and ARMA processes and is applied to identify the environmental system.

Underwater glider is an autonomous underwater vehicle that glides by controlling their buoyancy and attitude using internal actuators. By changing the vehicle's buoyancy intermittently, vertical motion can be achieved. Characteristics of glider motion include upward and downward movement in a saw tooth pattern, turning and gliding in a vertical spiral motion and gliding without using thrusters or propellers. This paper presents the modelling and identification on net buoyancy, depth and pitching angle of an underwater glider system. A ballast tank subsystem is considered appropriate for the identification process since it is the main parameter for the motion control. By selecting the ballast rate as the input, three aspects of the dynamics of a glider can be observed: buoyancy, depth of the glider and pitching angle. The MATLAB System Identification Toolbox TM is used to obtain a mathematical model of the glider ballast-buoyancy, ballast-depth and ballast-pitching angle conditioning system. The best three parametric estimation models are chosen, and the results of the comparison between simulated and estimated outputs are presented. The information obtained from the modelling and identification approaches are used for USM's Underwater Glider Prototype controller design. The information observed during this procedure are utilised for optimisation, stability, reliability and robustness analysis of the underwater glider.

Face recognition is one of the most important image processing research topics which is widely used in personal identification, verification and security applications. In this paper, a face recognition system, based on the principal component analysis (PCA) and the feedforward neural network is developed. The system consists of two phases which are the PCA preprocessing phase, and the neural network classification phase. PCA is applied to calculate the feature projection vector of a given face which is then used for face ...

This paper concerns dynamic neural networks for signal processing: architectural issues are considered but the paper focuses on learning algorithms that work on-line. Locally recurrent neural networks, namely MLP with IIR synapses and generalization of Local Feedback Multi-Layered Networks (LF MLN), are compared to more traditional neural networks, i.e. static MLP with input and/or output buffer (TDNN), FIR MLP and fully recurrent neural networks: simulations results are provided to compare locally recurrent neural networks with TDNN and FIR MLP. Moreover, we propose a new learning algorithm, based on the Back Propagation Through Time and called Causal Back Propagation Through Time (CBPTT), that is faster and more stable than the algorithm previously used for IIR MLP. The algorithm that we propose includes as particular cases the following algorithms: Wan's Temporal Back Propagation, Back Propagation for Sequences (BPS) and Back-Tsoi algorithm.

The paper presents a short survey of three topics: modern sensor networks, distributed parameter systems and estimation techniques, especially using artificial intelligence tools, to be involved in the new domain of identification of distributed parameter systems, based on sensor networks and artificial intelligence. As smart and small devices the modern sensors are capable to be implemented in large distributed parameter systems. Sensor networks, with hundred and thousands of ad-hoc tiny sensor nodes spread across a geographical, are acting as a distributed sensor in a distributed parameter system. Sensor network topics, sensor network architectures and sensor network applications are presented. Examples of distributed parameter systems with large application in practice as the process of heat conduction, applications related to electricity domain, motion of fluids, the processes of cooling and drying, phenomenon of diffusion and other applications are presented. The identification...

Noise is usually present in dynamic data, either as a result of measurement errors or as a combined effect of many different unknown factors. It is possible to model dynamic systems using regression errors, where the residual error is minimized, assuming that all the noise is additive. Other approaches involve filtering the noise (i.e. smoothing the data) in order to improve the fitness of the model. However, sometimes the dynamic behavior is the result of pure random processes, as it is the case of Markovian processes (e.g. random walks, Brownian motion, etc.). These systems present an apparent deterministic behavior but which is actually caused by randomness. The first challenge dealt in this work is the correct identification of true from apparent determinism, using only the dynamic data available. A second challenge is improving the model of the system (i.e. increasing its goodness-of-fit), combining both deterministic and random effects into a single randomistic model. A general method is proposed for analyzing any set of dynamic data, identifying the critical derivative corresponding to the state of the dynamic system where determinism and randomness are both significant. Higher-order derivatives will present a predominant random behavior, whereas lower-order derivatives (or integrals) will be predominantly deterministic as long as the integral of the critical derivative is not a pure Markovian random variable. A test for Markovian behavior is also proposed for identifying true determinism. Six case studies are presented in order to exemplify the method proposed. In the first three cases, the data is obtained from dynamic Monte Carlo simulations. In the last three cases, data is taken from real life examples: Global temperature change, USD/EUR exchange rate, and bodyweight loss.

We undertake network efficiency analysis within an input-output model that allows us to assess potential technical efficiency gains by comparing technologies corresponding to different economies. Input-output tables represent a network where different sectoral nodes use primary inputs (endowments) to produce intermediate input and outputs (according to sectoral technologies), and satisfy final demand (preferences). Within the input-output framework it is possible to optimize primary inputs allocation, intermediate production and final demand production by way of non-parametric data envelopment analysis (DEA) techniques. DEA allows us to model the different subtechnologies corresponding to alternative production processes, to assess efficient resource allocation among them, and to determine potential output gains if inefficiencies were dealt with. The proposed model optimizes the underlying multi-stage technologies that the input-output system comprises identifying the best practice economies. The model is applied to a set of OECD countries.

The hysteresis behavior of a linear stroke magnetorheologica l damper is characterized for several magnetic elds and sinusoidal excitations over a nominal operational frequency range of 1.0-3.0 Hz. The behavior of the damper is inadequately modeled using the equivalent viscous damping and the complex modulus. Therefore, four different nonlinear modeling perspectives are discussed for purposes of system identi cation procedures, including the 1) nonlinear Bingham plastic model, 2) nonlinear biviscous model, 3) nonlinear hysteretic biviscous model, and 4) nonlinear viscoelastic plastic model. The rst three nonlinear models are piecewise continuous in velocity. The fourth model is piecewise smooth in velocity. The parameters for each model are identi ed from an identi cation set of experimental data; these parameters are then used to reconstruct the force vs displacement and the force vs velocity hysteresis cycles for the respective model. Model performance is evaluated by calculating equivalent viscous damping and force time history errors between the model t and the experimental data. In addition to the identi cation study, a validation study was done. Model parameters were calculated for offset values of current and frequency. These intermediate parameters were used to calculate hysteresis cycles, which were compared with a second set of experimental data, a validation data set. Identi cation and validation study results including damping levels and force time history errors.

Adaptive algorithms are succeptible to noise signals due to the perturbation of the gradient direction. Depending upon the statistics of the noise, various cost functions can be minimised. The Least Mean Kurtosis (LMK) adaptive algorithm is a stochastic gradient approach that uses the kurtosis of the error signal. Depending upon the noise distribution, the forgetting factor used in the LMK algorithm must be chosen optimally. In this paper, a version of LMK is presented, in which the forgetting factor is made time-varying such that if the output-noise distribution is unknown, which is almost always the case in practice, the algorithm will tune its forgetting factor in sympathy with the noise distribution. Hence the proposed algorithm is more flexible than the Least Mean Square (LMS) and Least Mean Fourth (LMF) adaptive algorithms under noisy conditions.

This paper presents the results of an experimental and theoretical investigation on the Pietratagliata cable-stayed bridge (Udine, Italy). Ambient vibration tests were performed in order to estimate the dynamic characteristics of the lower vibration modes of the bridge. Structural identification is carried out by means of a manual tuning procedure based on finite element models of increasingly accuracy. The analysis allows to improve the description of boundary conditions and mechanical interaction between the bridge components. Results from local dynamic testing are used to estimate the traction on the cables and to assess the integrity of the suspending system of the bridge.

In this study, compression tests were conducted to determine the flow behavior of various types of Plasticine. True stress-true strain relations are presented in the form of a well-known power law equation. It is shown that the strength coefficient and the strain hardening exponent vary significantly among the Plasticine with different colors commercially available. Furthermore, since three dimensional simulations require arranging slabs of alternating colors to form a grid pattern, two different colors, exhibiting similar flow characteristics, are identified. The flow behavior of the composite model is usually not the same as that of the individual colors. The results presented in this paper allow selection of the type of Plasticine which most closely resembles the strain hardening characteristic of the actual metal, thereby satisfying the "similarity condition" of the Physical Modeling Technique.

The paper focuses on parameter estimation processes for physically meaningful models tuned online and define a process to determine whether a model is relevant or not for GMBA-BEMS tuning purpose. The proposed approach relies on the data coming from the PREDIS/MHI platform. The first step is to calculate realistic parameters with possible intervals because nonlinear optimization, required for physically explicit models, implies initial parameters. The next step is to find the best reduced order model structure using an iterative nonlinear optimization approach using recorded data that leads to parameter estimation. It is based on randomize initial values for parameters to measure the convexity of the search space in studying the convergence. Finally, the last step consists in enhancing the time zones where reduced order model does not fit well with the available data. It points out some non-modeled phenomena. It is based on a weighted iterative estimation method where weights depend on the estimation errors obtain at the previous step.

This study presents an optimal design for magnetorheological (MR) valves for minimizing the control energy to be applied to coils to control the pressure drop of the valves. The optimization problem identifies parameters such as applied current, coil wire size and geometric dimensions of the valves which satisfy the specified pressure drop and inductive time constant requirements. After describing the configuration of MR valves, their pressure drops are obtained on the basis of the Bingham model of MR fluid. Then, the control energy which is an objective function and the inductive time constant are derived. Subsequently, an optimization procedure using a golden-section algorithm and a local quadratic fitting technique is constructed via a commercial finite element method parametric design language. Using the optimization tool developed in this study, optimal MR valve configurations are identified, which are constrained to a specific cylindrical volume defined by its radius and height. In addition, optimization results for MR valves with different required pressure drops and different constrained volumes are obtained and presented.

The design of state estimators for nonlinear dynamic systems affected by disturbances is addressed in a functional optimization framework. The estimator contains an innovation function that has to be chosen within a suitably defined class of functions in such a way to minimize a cost functional given by the worst-case ratio of the L p norms of the estimation error and the disturbances. Since this entails an infinite-dimensional optimization problem that under general hypotheses cannot be solved analytically, an approximate solution is sought by minimizing the cost functional over linear combinations of simple "basis functions," represented by computational units with adjustable parameters. The selection of the parameters is made by solving a constrained nonlinear programming problem, where the constraints are given by pointwise conditions that ensure the well-definiteness of the functional and the existence of a solution. Penalty terms are introduced in the cost function to account for constraints imposed on points that result from sampling the sets to which the trajectories of the state and of the estimation error belong. To ensure an efficient 446 J Optim Theory Appl : covering of the sets, low-discrepancy sampling techniques are exploited that generate samples deterministically spread in a uniform way, without leaving regions of the space undersampled.

A system identi cation method to extract the disturbance-free dynamics and the disturbance effect correctly despite the presence of unknown periodic disturbances is presented. The disturbance frequencies and waveforms can be completely unknown and arbitrary. Only measurements of the excitation input and the disturbancecontaminated response are used for identi cation. Initially, the disturbances are modeled implicitly. When the order of an assumed input-output model exceeds a certain minimum value, the disturbance information is completely absorbed in the identi ed model coef cients. A special interaction matrix explains the mechanism by which information about the system and the disturbances are intertwined and more importantly, how they can be separated uniquely and exactly for later use in identi cation and control. From the identi ed information a feedforward controller can be developed to reject the unwanted disturbances without requiring the measurement of a separate disturbance-correlated signal. The multi-input multi-output formulation is rst derived in the deterministic setting for which the system and disturbance identi cation is exact. Extensions to handle noise-contaminated data are also provided. Experimental results illustrate the method on a exible structure. A companion paper addresses the problem where the disturbance effect is modeled explicitly.

A new spacecraft attitude estimation approach based on the Unscented Filter is derived. For nonlinear systems the Unscented Filter uses a carefully selected set of sample points to more accurately map the probability distribution than the linearization of the standard Extended Kalman Filter, leading to faster convergence from inaccurate initial conditions in attitude estimation problems. The filter formulation is based on standard attitude-vector measurements using a gyro-based model for attitude propagation. The global attitude parameterization is given by a quaternion, while a generalized three-dimensional attitude representation is used to define the local attitude error. A multiplicative quaternion-error approach is derived from the local attitude error, which guarantees that quaternion normalization is maintained in the filter. Simulation results indicate that the Unscented Filter is more robust than the Extended Kalman Filter under realistic initial attitude-error conditions. ...

This paper presents a new approach to the system identification of a two-input two-output multivariable system. In a similar manner to that used for the single-input/singleoutput system, the multi-input multi-output (MIMO) system response may be obtained directly from measured input output frequency responses. An investigation of compact multifrequency data measurement signals, which have been extensively cataloged by the authors, has provided compact multifrequency ternary and quaternary input signals that have uncorrelated spectra. They are powerful multisymbol, multilevel computer generated measurement signals whose signal power is concentrated in either two or three dominant harmonics. As the signals, which are given in this paper, have six, seven, or eight symbols in their measurement codes, both the computation time for the frequency estimates and the experimental time are minimized. Two multifrequency quaternary signals with uncorrelated spectra are used to identify a simulated distillation column. It is shown that the cross coupling terms between the measurement channels may be removed by these digital measurement signals with the same number of symbols but different measurement codes.

The roll dynamics of a 5kg, 1.3 m high sounding rocket are analyzed in a vertical wind tunnel. Significant turbulence in the tunnel makes the system identification of the effective inertia, damping and asymmetry with respect to roll challenging. A novel method is developed which decouples the disturbance from the rocket frame's intrinsic roll dynamics and allows accurate prediction of roll rate and angle. The parameter identification method is integral-based, and treats wind disturbances as equivalent to a movement in the actuator fins. The method is robust, requires minimal computation, and gave a realistic disturbance distribution reflecting the randomness of the turbulent wind flow. The mean absolute roll rate of the rocket frame observed in experiments was 16.4 degree/s and the model predicted the roll rate with a median error of 0.51 degrees/s with a 90th percentile of 1.25 degrees/s. The roll angle (measured by an encoder), was tracked by the model with a median absolute error of 0.25 degrees and a 90th percentile of 0.50 degrees. These results prove the concept of this minimal modeling approach which will be extended to pitch and yaw dynamics in the future.

The optimal fractional system identification is a challenging problem as it requires the estimation of not only the numerator parameters, but also the poles transfer function model and the non-integer order reading to complex nonlinear optimization. In this paper, an algorithm using the least square method, called ''Vector Fitting" (V.F.) developed by Gustavsen is extended to the fractional order system identification in frequency domain. This algorithm proceeds recursively contrarily to the well known Levy's algorithm which uses only one iteration to calculate the model parameters. The use of an iterative method efficiently directs the model parameters evolution towards their optimal values. Indeed, during iteration the poles of the model are calculated and used as starting poles for the next one, the stability of the identified model can thus be imposed. The V.F. algorithm is then associated with the heuristic optimization method: particle swarm optimization (PSO), leading to a new fractional system identification algorithm. The algorithm works in a hierarchical way; in a higher level, PSO determines the non-integer order and in a lower stage, the V.F. algorithm identifies the other parameters.

Recently, the quadcopter configuration is becoming prevalent for both civilian and military applications, and research is continuing to improve its controllability. However, most the control methodologies depend on accurate system models which are derived from six degrees of freedom nonlinear system dynamics and they generally do not consider realistic outdoor perturbations and uncertainties. As a solution, model-free data-driven system identification and controlling of quadcopter have been refined in this paper. This nonlinear multiple input-multiple output system identification is proliferated using a fuzzy clustering model. The accuracy of the identification is seen to very high. The results of a PID controller used in conjunction with the identified fuzzy model is also presented. The PID controller with the fuzzy identified model is seen to precisely follow the commanded signals.

In this paper, two compact lumped element models synthesis methods for distributed passive electromagnetic structures are presented. The first method is based on the canonical Foster representation while the second method uses the Brune's synthesis procedure which is a more general approach capable to account for lossy structures. Both methods, used in combination with a system identification approach, generate equivalent network models that can be analyzed using circuit simulators. In addition, two decomposition techniques, allow to reduce complexity in the network model synthesis by exploiting the structure's symmetry are presented. The accuracy and efficiency of the proposed methods are demonstrated on two examples: a lossless branch-line directional coupler and a lossy lowpass microstrip filter.

The prevailing Operational Modal Analysis (OMA) techniques provide in most cases reasonably accurate estimates of structural frequencies and mode shapes. In contrast though, they are known to often produce poor structural damping estimates, which is mainly due to inherent random and/or bias errors. In this paper a comparison is made of the effectiveness of three existing OMA techniques in providing accurate damping estimates for varying loadings, levels of noise, number of added measurement channels and structural damping. The evaluated techniques are derived in the time domain and are namely the Ibrahim Time Domain (ITD), Eigenvalue Realization Algorithm (ERA) and the Polyreference Time Domain (PTD). The response of a two degree-of-freedom (2DOF) system is numerically established from specified modal parameters with well separated and closely spaced modes. Two types of response are considered, free response and random response from white noise loading. Finally, the results of the numerical study are presented, in which the error of the structural damping estimates obtained by each OMA technique is shown for a range of damping levels. From this, it is clear that there are notable differences in accuracy between the different techniques.

This research investigates the current Identification and Branding System of Egypt’s Governorates. It discusses the core elements of each governorate and how it is branded. The research reviewes the historical branding of Egypt as a nation as well as the different comparative case studies of United Arab Emirates, Switzerland, South Korea and Turkey. There are also different design principles that are discussed through the research, which should be applied when designing such an identification system for a governorate or a city.

In this paper, several instrumental variable (IV) and instrumental variable-related methods for closed-loop system identification are considered and set in an extended IV framework. Extended IV methods require the appropriate choice of particular design variables, as the number and type of instrumental signals, data prefiltering and the choice of an appropriate norm of the extended IV-criterion. The optimal IV estimator achieves minimum variance, but requires the exact knowledge of the noise model. For the closed-loop situation several IV methods are put in an extended IV framework and characterized by different choices of design variables. Their variance properties are considered and illustrated with a simulation example.

The battery management system (BMS) is an integral part of an automobile. It protects the battery from damage, predicts battery life, and maintains the battery in an operational condition. The BMS performs these tasks by integrating one or more of the functions, such as protecting the cell, thermal management, controlling the charge-discharge, determining the state of charge (SOC), state of health (SOH), and remaining useful life (RUL) of the battery, cell balancing, data acquisition, communication with on-board and off-board modules, as well as monitoring and storing historical data. In this paper, we propose a BMS that estimates the critical characteristics of the battery (such as SOC, SOH, and RUL) using a data-driven approach. Our estimation procedure is based on a modified Randles circuit model consisting of resistors, a capacitor, the Warburg impedance for electrochemical impedance spectroscopy test data, and a lumped parameter model for hybrid pulse power characterization test data. The resistors in a Randles circuit model usually characterize the self-discharge and internal resistance of the battery, the capacitor generally represents the charge stored in the battery, and the Warburg impedance represents the diffusion phenomenon. The Randles circuit parameters are estimated using a frequency-selective nonlinear least squares estimation technique, while the lumped parameter model parameters are estimated by the prediction error minimization method. We investigate the use of support vector machines (SVMs) to predict the capacity fade and power fade, which characterize the SOH of a battery, as well as estimate the SOC of the battery. An alternate procedure for estimating the power fade and energy fade from low-current Hybrid Pulse Power characterization (L-HPPC) test data using the lumped parameter battery model has been proposed. Predictions of RUL of the battery are obtained by support vector regression of the power fade and capacity fade estimates. Survival function estimates for reliability analysis of the battery are obtained using a hidden Markov model (HMM) trained using time-dependent estimates of capacity fade and power fade as observations. The proposed framework provides a systematic way for estimating relevant battery characteristics with a high-degree of accuracy.

In its simplest form, the human postural control system can be described as a closed-loop control system consisting of a plant (body segments and musculotendon actuators) and feedback. Previous efforts to understand the contributions of plant and feedback employed techniques to "open the loop" which is problematic with the study of posture because the plant is unstable without feedback. In

A new dynamic time-delay fuzzy wavelet neural network model is presented for nonparametric identification of structures using the nonlinear autoregressive moving average with exogenous inputs approach. The model is based on the integration of four different computing concepts: dynamic time delay neural network, wavelet, fuzzy logic, and the reconstructed state space concept from the chaos theory. Noise in the signals is removed using the discrete wavelet packet transform method. In order to preserve the dynamics of time series, the reconstructed state space concept from the chaos theory is employed to construct the input vector. In addition to denoising, wavelets are employed in combination with two soft computing techniques, neural networks and fuzzy logic, to create a new pattern recognition model to capture the characteristics of the time series sensor data accurately and efficiently. The model balances the global and local influences of the training data and incorporates the imprecision existing in the sensor data effectively. Experimental results on a five-story steel frame are employed to validate the computational model and demonstrate its accuracy and efficiency.

A novel hybrid adaptive FIR/IIR filter configuration is presented. The aim is to reduce the main obstacles limiting the use of adaptive IIR filters: namely convergence and stability. The hybrid scheme attempts to exploit the good qualities of both adaptive FIR filters (good convergence and stability) and those for adaptive IIR filters (sharp transition bands, lower order, and complexity). In this paper, the hybrid scheme is presented for a system identification problem, and the task is achieved in three main steps: a) A training signal is applied to an adaptive FIR filter to achieve a near optimum approximation; b) the FIR coefficients are mapped through the balanced model reduction technique to a smaller set of coefficients initializing an adaptive IIR filter; c) there is a process of fine tuning (small adaptive stepsize) the adaptive IIR coefficients for further approximation precision. The IIR filter in the hybrid scheme makes use of the Steiglitz McBride algorithm that assures the stability of the filter. Furthermore, the convergence to the minimum error performance is enforced by the fact that the adaptive IIR filter is operating close to the optimum, requiring very small pole perturbation.

This paper addresses two problems in structural damage identification: locating damage and assessing damage severity, which are incorporated into the classification framework based on the theory of sparse representation (SR) and compressed sensing (CS). The sparsity nature implied in the classification problem itself is exploited, establishing a sparse representation framework for damage identification. Specifically, the proposed method consists of two steps: feature extraction and classification. In the feature extraction step, the modal features of both the test structure and the reference structure model are first blindly extracted by the unsupervised complexity pursuit (CP) algorithm. Then in the classification step, expressing the test modal feature as a linear combination of the bases of the over-complete reference feature dictionary-constructed by concatenating all modal features of all candidate damage classes-builds a highly underdetermined linear system of equations with an underlying sparse representation, which can be correctly recovered by ℓ 1 -minimization; the non-zero entry in the recovered sparse representation directly assigns the damage class which the test structure (feature) belongs to. The two-step CP-SR damage identification method alleviates the training process required by traditional pattern recognition based methods. In addition, the reference feature dictionary can be of small size by formulating the issues of locating damage and assessing damage extent as a two-stage procedure and by taking advantage of the robustness of the SR framework. Numerical simulations and experimental study are conducted to verify the developed CP-SR method. The problems of identifying multiple damage, using limited sensors and partial features, and the performance under heavy noise and random excitation are investigated, and promising results are obtained. (S. Nagarajaiah).

A method for measuring mooring line tension is proposed based on observation of the natural frequencies of the mooring line segment between the winch and the fairlead. The anchor line tension is observed through the string equation where an analytical expression for the line's eigenfrequencies is obtained. The tension is observed on line by utilizing a nonparametric system identification approach in which the peaks of a maximum entropy spectrum of the transverse acceleration measures of the vibrating string are automatically identified. The method is verified against full-scale data from the Troll B floating concrete oil production platform operating in the North Sea.

Fault detection and isolation (FDI) of a class of networked control systems (NCS), applied for telerobotics system is studied in this paper. The considered NCS application is related to telerobotics system, where it is modelled with a hybrid manner, by including the continuous, discrete, uncertain, and stochastic aspects of all the system components. The main considered components of the NCS namely the network system and controlled system are completely decoupled according to their operation characteristics. The network part is taken as a discrete and stochastic system in presence of non-structured uncertainties and external faults, while the controlled part is considered as a continuous system in presence of input and output faults. Two model based fault diagnosis approaches are proposed in this paper. The first concerns a discrete and stochastic observer applied to the network system in order to detect and isolate system faults in presence of induced delay on the network part. The second is based on the analytical redundancy relations (ARR) allows detecting and isolating the input and output system' faults. Experimental results applied on telerobotics system, show the performance and the limit of the proposed fault diagnosis approach.

Vector fitting (VF) is a popular iterative rational approximation technique for sampled data in the frequency domain. VF is nowadays widely investigated and used in the Power Systems and Microwave Engineering communities. The VF methodology is recognized as an elegant version of the Sanathanan-Koerner iteration with a well-chosen basis.

Underwater glider is an autonomous underwater vehicle that glides by controlling their buoyancy and attitude using internal actuators. By changing the vehicle's buoyancy intermittently, vertical motion can be achieved. Characteristics of glider motion include upward and downward movement in a saw tooth pattern, turning and gliding in a vertical spiral motion and gliding without using thrusters or propellers. This paper presents the modelling and identification on net buoyancy, depth and pitching angle of an underwater glider system. A ballast tank subsystem is considered appropriate for the identification process since it is the main parameter for the motion control. By selecting the ballast rate as the input, three aspects of the dynamics of a glider can be observed: buoyancy, depth of the glider and pitching angle. The MATLAB System Identification Toolbox TM is used to obtain a mathematical model of the glider ballast-buoyancy, ballast-depth and ballast-pitching angle conditioning system. The best three parametric estimation models are chosen, and the results of the comparison between simulated and estimated outputs are presented. The information obtained from the modelling and identification approaches are used for USM's Underwater Glider Prototype controller design. The information observed during this procedure are utilised for optimisation, stability, reliability and robustness analysis of the underwater glider.