This is the author pre-publication version. This paper does not include the changes arising from the revision, formatting and publishing
processes. The final version that should be used for referencing is:
A. Gardi, M. Marino, S. Ramasamy, T. Kistan, R. Sabatini, “4-Dimensional Trajectory Optimisation Algorithm for Air Traffic Management
Systems”, IEEE/AIAA 35th Digital Avionics Systems Conference (DASC), Sacramento, CA, USA, 2016.
4-Dimensional Trajectory Optimisation Algorithm for
Air Traffic Management Systems
Alessandro Gardi, Matthew Marino, Subramaniam
Ramasamy and Roberto Sabatini
RMIT University – School of Engineering
Melbourne, VIC 3000, Australia
Abstract—This paper presents Multi Objective Trajectory
Optimization (MOTO) algorithms that were developed for
integration in state-of-the-art Air Traffic Management (ATM)
and Air Traffic Flow Management (ATFM) systems. The MOTO
algorithms are conceived for the automation-assisted replanning
of 4-Dimensional Trajectories (4DT) when unforeseen
perturbations arise at strategic and tactical online operational
timeframes. The MOTO algorithms take into account updated
weather and neighbouring traffic data, as well as the related
forecasts from selected sources. Multiple user-defined
operational, economic and environmental objectives can be
integrated as necessary. Two different MOTO algorithms are
developed for future implementation in ATM systems: an enroute variant and a Terminal Manoeuvring Area (TMA) variant.
In particular, the automated optimal 4DT replanning algorithm
for en-route airspace operations is restricted to constant flight
level to avoid violating the current vertical airspace structure. As
such, the complexity of the generated trajectories reduces to 2
dimensions plus time (2D+T), which are optimally represented in
the present 2D ATM display formats. Departing traffic
operations will also significantly benefit from MOTO-4D by
enabling steep/continuous climb operations with optimal throttle,
reducing perceived noise and gaseous emissions.
Keywords—4-Dimensional
Trajectory;
Air
Traffic
Management; Decision Support System; Trajectory Optimization
I. INTRODUCTION
More effective and comprehensive implementations of
flight trajectory optimisation techniques are being considered
as a very promising pathway to enhance the efficiency and
flexibility of online air traffic operations both for short and
long-haul flights. The fundamental proposition is not new, but
state-of-the-art flight planning methods are still based on the
optimised vertical planning techniques initially developed in
the 1970s and on lateral path planning based on optimal wind
routing [1-6]. The known limitations are associated to the small
set of optimality criteria (currently only fuel- and time-costs)
and to the fact that the initial flight plan is the static entity
assumed as a reference for every subsequent amendment. As a
result, the limited initial optimality is progressively
compromised when strategic or tactical Air Traffic
Management (ATM) and Air Traffic Flow Management
(ATFM) amendments are introduced. Suitably defined models
can replicate the various operational and environmental aspects
The work presented in this article was supported by THALES Australia
under RMIT University Contract ID 0200312837
Trevor Kistan
THALES Australia – Air Traffic Management
Melbourne, VIC 3000, Australia
that depend on the flown aircraft trajectory, allowing for more
comprehensive and real-time Multi-Objective Trajectory
Optimisation (MOTO) techniques to be integrated in airborne
avionics and ground-based Communication, Navigation,
Surveillance and ATM (CNS/ATM) Decision Support Systems
(DSS). These novel DSS integrating real-time MOTO
capabilities have the potential to support a further enhanced
exploitation of airspace and airport capacities, which is
emerging thanks to the advances in CNS technologies.
This paper describes two dedicated MOTO functionalities
for integration in state-of-the-art ATM and ATFM DSS. The
MOTO algorithms are conceived for the automation-assisted
replanning of 4-Dimensional Trajectories (4DT) when
unforeseen perturbations arise at strategic and tactical online
operational timeframes. The MOTO algorithms take into
account updated weather and neighbouring traffic data, as well
as the related forecasts from selected sources. Multiple
operational, economic and environmental objectives can be
user-defined as necessary. The project specifically addresses
current and short-term future ATM operational paradigms and
regulations and thus two different MOTO algorithms are
developed: an en-route variant and a Terminal Maneuvering
Area (TMA) variant. In particular, the automated optimal 4DT
replanning algorithm for en-route airspace operations is
restricted to constant flight level to avoid violating the current
vertical airspace structure. As such, the complexity of the
generated trajectories reduces to 2 dimensions plus time
(2D+T), which are optimally represented in the present 2D
ATM display formats. The Air Traffic Controller (ATCo) may
amend the flight level of an optimized 2D+T trajectory in the
traditional manner if necessary. The constant flight level
limitation will theoretically produce a sub-optimal flight
trajectory, however, the computed trajectory will be more
efficient than vectors as the atmospheric wind field can be
exploited to maximize aircraft ground speed while reducing
fuel burn and emissions. The operational, economic and
environmental benefits from MOTO are maximized in the
TMA variant as the full 4DT MOTO (MOTO-4D) routines are
exploited to increase the operational efficiency and reduce fuel
burn, emissions and noise. In the current paradigm aircraft are
handled on a "first come first serve" basis where multiple
objectives can be user defined and applied to the optimization
problem. However, an evolution to "just in time" 4DT based
operations in the TMA is supported by the MOTO
functionalities. As a consequence, TMA operations are allowed
to evolve and become more efficient by minimising scheduling
delays, providing automated path-stretching, reducing
pollutants/noise emissions and supporting continuous descent
approaches. As a consequence, ATCo workload will be
relieved thanks to automation-assisted deconfliction and path
planning, and this is expected to increase operational safety.
Departing traffic operations will also significantly benefit from
MOTO-4D by enabling steep/continuous climb operations with
optimal throttle, reducing perceived noise and gaseous
emissions.
II. OPTIMAL CONTROL FORMULATION
Trajectory optimisation studies methods to determine the
best possible trajectory of a dynamical system in a finitedimensional manifold, in terms of specific objectives and
adhering to given constraints and boundary conditions [7]. This
definition corresponds to the definition of Optimal Control
Problems (OCP), and consequently the most traditional and
theoretically rigorous way to pose a Trajectory Optimisation
Problem (TOP) is based on the optimal control theory. Most
OCP solution methods are conventionally categorised as either
direct methods if based on the transcription to a finite NonLinear Programming (NLP) problem, or indirect methods if
theoretical derivations based on the calculus of variation are
implemented to formulate a Boundary-Value-Problem (BVP)
[8-11]. An additional class of OCP solution strategies is
represented by heuristic methods. The optimal control
formulation of TOP is based on a scalar time
and on
vectors of time-dependent state variables
, timedependent control variables
and system
parameters
. Based on these, the following components
are defined: dynamic constraints, path constraints, boundary
conditions and cost functions. These components characterise a
well-posed OCP and guide the selection of an appropriate
numerical solution method and multi-objective decision logic.
Dynamic constraints describe the feasible motion of the system
(i.e. the aircraft, in our case) within the TOP as in:
̇
(1)
All non-differential constraints insisting on the system
between the initial and final conditions are classified as path
constraints, as they restrict the path of states and controls of the
dynamical system. A generalised expression accounting for
both equality and inequality constraints is [12]:
(2)
Boundary conditions define the values that state and control
variables of the dynamical system shall have at the initial and
final times. A generalised expression also accounting for
relaxed boundary conditions is:
( )
( )
(3)
Performance indexes quantify the achievement of a
particular objective by means of suitably defined cost
functions. The generic formulation of a performance index
that takes into account both integral and terminal costs was
introduced by Bolza [13-15], and is expressed as:
[ ( )
( ) ]
∫
(4)
The optimisation is classified as single-objective when an
individual performance index J is introduced and multiobjective when two or multiple performance indexes Ji are
defined. Different objectives can be conflicting, that is the
attainment of a better
would lead to a worse
{
[
]
} . Hence, the optimisation in terms of
two or more objectives generates a number of possible
compromise choices, for which a trade-off decision logic must
be adopted to identify an individual solution.
The first step of the theoretical derivations required by
indirect methods to formulate a BVP problem consists in the
Lagrangian relaxation as in:
( ( )
)
( ) ( )
∫ {
̇
Where are the Lagrangian multipliers and
the Hamiltonian function, defined as:
}
(5)
is
(6)
In addition to being mathematically complex, the
application of all the analytical steps involved in indirect
methods lead to very different BVP depending on the initial
problem statement and this aspect limits the flexibility of
indirect methods. Some notable examples are presented in [15].
Finally, since considerable nonlinearities are present in most
aerospace TOP, the reduction of nontrivial cases into linear
quadratic BVP is typically precluded, and therefore solutions
have to be attempted by either iterative methods, which
conventionally exhibit non-global convergence, or by heuristic
solution techniques.
Direct methods, on the other hand, prescribe the immediate
parameterisation of states and controls in the case of direct
collocation methods, or of controls only in the case of direct
shooting. This involves adopting a basis of known linearly
independent functions
with unknown coefficients
in
the general form:
∑
(7)
In global collocation (pseudospectral) methods, the
evaluation of state and control vectors is performed at discrete
collocation points across the problem domain using suitably
defined orthogonal (spectral) interpolating functions [11, 16].
Similarly to the nonlinear BVP arising in indirect solution
approaches, the NLP problems encountered in the direct
solution of TOP are typically solved by iterative algorithms, or
through some kind of heuristics. The initial steps involve the
adoption of an n-dimensional Taylor series expansion of F(x)
to the third term as in:
(
)
(
(
)
(
where:
)
(8)
)
force T [N]; load factor N [ ]; bank angle
[rad]. Other
variables and parameters include: aircraft weight
and
aerodynamic drag D [N]; wind velocity vw in its three scalar
components [m s-1]; gravitational acceleration g [m s-2]; Earth
radius RE [m]; fuel flow FF [kg s-1] and thrust angle of attack
[rad]. The aerodynamic drag is modelled as:
(9)
[
(
)
]
(10)
(11)
[
(14)
where
is the local air density retrieved
from weather input data grid or a weather model, S is the
reference wing surface,
and
are the parabolic drag
coefficients typically available from aircraft performance
databases such as Eurocontrol’s Base of Aircraft Data
(BADA). The lift coefficient can be calculated from:
(15)
]
An iterative NLP solution can thus be formulated so that the
search direction at step k based on the n-dimensional Newton
method is written:
The thrust force control variable is most frequently
expressed as the product of the throttle coefficient (defined as
dimensionless and ranging between 0 and 1), and the maximum
thrust
as in:
(12)
Various factors have to be considered when developing
computationally efficient NLP solution strategies and some
further detail is given in [17, 18].
A. Optimality criteria and dynamic constraints
Optimality criteria and constraints are introduced in the
optimisation problem by means of suitable models. Three
degrees of freedom (3-DOF) point-mass flight dynamics
models are currently preferred for TOP involving mediumlarge transport aircraft and for possible avionics and ATM
system implementations, as the overall size of the resulting
NLP is considerably lower than in the case of six degrees of
freedom (6-DOF). A fairly comprehensive 3-DOF formulation
used in our MOTO algorithms assumes variable aircraft mass,
constant vertical gravity and the effects of winds and is
therefore written as:
̇
̇
̇
̇
̇
[(
)
]
(13)
̇
{ ̇
where the state vector consists of: longitudinal velocity v
[m s-1], flight path angle γ [rad]; track angle χ [rad]; geographic
latitude [rad]; geographic longitude [rad]; altitude z [m];
aircraft mass m [kg]; and the control vector includes: thrust
(16)
This allows a natural nondimensionalisation of the control
variable. For turbofan aircraft, the following empirical
expressions were adopted in the development of BADA, to
determine the climb thrust and the fuel flow
, which
operationally equates to the maximum thrust
in all flight
phases excluding take-off [19]:
(
)
(17)
(18)
[
]
where is the throttle control,
is the geopotential
pressure altitude in feet,
is the deviation from the standard
atmosphere temperature in kelvin,
is the true airspeed.
are the empirical thrust and fuel flow
coefficients, which are also supplied as part of BADA for a
considerable number of currently operating aircraft [19].
In order to calculate pollutant emissions as a function of the
fuel flow, the emission index
is introduced as per the
following definition:
∫
(19)
where the generic Gaseous Pollutant (GP) should be
replaced by the specific one being investigated. While carbon
dioxide (CO2) emissions are characterised by an approximately
, an
constant emission index of
empirical model for carbon monoxide (CO) and unburned
hydrocarbons (HC) emission indexes (
) in [g/Kg] at
mean sea level based on nonlinear fit of experimental data from
the ICAO emissions databank is:
(20)
where the fitting parameters
accounting for the
emissions of 165 currently operated civil turbofan engines are
{
} for CO and
{
} for HC [20]. The nitrogen oxides (NOX) emission index
[g/Kg] based on the curve fitting of 177 currently operated civil
aircraft engines is [20]:
(21)
III. MULTI-OBJECTIVE OPTIMALITY
In the aviation domain, single and bi-objective optimisation
techniques have been exploited for decades but they accounted
only for flight time-related costs and fuel-related costs. These
techniques have also been implemented in a number of current
generation FMS in terms of the Cost Index (CI), that allowed
an optimal selection of Calibrated Air Speed (CAS) / Mach
number based on time and fuel costs only.
Conflicting objectives arise when introducing multiple
environmental, economic and operational criteria in our MOTO
algorithms. Furthermore, the implementation of constraints that
are either unfeasible or contrasting the attainment of better
optimality also has to be addressed by adopting suitable multiobjective optimality techniques. These can either be expressed
a priori (i.e. beforehand) or a posteriori (i.e. afterwards). A
priori methods analyse ways to articulate the preferences to
identify a combined objective which is then supplied to a
single-objective TOP solution algorithm, and include weighted
global criterion (including the simple weighted sum), weighted
min-max, weighted product, exponential weighted criterion,
lexicographic and physical programming methods [7, 21]. A
posteriori methods allow the user (or suitably defined decision
logics) to select an individual optimal solution from a Pareto
front of trade-off choices, and include normal boundary
intersection, normal constraint and physical programming
methods [7, 21].
In general, a specific performance objective can be defined
for each route segment. This performance objective is a multiobjective generalisation of the CI. In general, the weightings
can be varied dynamically among the different flight phases of
the flight. Since computational times are a crucial aspect in
online 4DT planning applications, an a priori articulation of
preference involving the weighted sum of the various
performance indexes Ji is employed to combine the multiple
conflicting operational, economic and environmental
objectives. For a more detailed discussion the reader is referred
to [7, 9, 11].
IV. MOTO FOR TERMINAL MANOEUVRING AREA
The MOTO for Terminal Sequencing and Spacing (TSS) is
based on the optimal control formulation presented in section 2.
The optimal control formulation shows its best potential in this
context as this is a truly 4D application where multiple
variables and constraints are at play at the same time. The
adoption of optimal control-based techniques in the TSS
context allows accurate continuous descent profiles to be
natively generated, CO/HC emissions to be minimised, as well
as optimised path stretching to be introduced as necessary to
achieve the set constraints. The challenges associated with
optimal control-based MOTO implementations lie in the
characteristics of the generated trajectory. More specifically,
the mathematically optimal 4DT generated as output by these
algorithms is a discretised Continuous/Piecewise Smooth
(CPWS) curve, which in general may not be flyable by human
pilots nor by conventional Automatic Flight Control Systems
(AFCS), as it includes transition manoeuvres involving
multiple simultaneous variations in the control inputs.
Although continuous, the variations in control input profiles
can also be particularly abrupt, exceeding what can be
practically and safely feasible in the modelled aircraft.
Moreover, the discretised CPWS consists of a very high
number of 4D waypoints, which would have unacceptable
impacts on the bandwidth of the data-link where they would be
exchanged. Therefore, a post-processing stage is introduced to
segment the discretised CPWS trajectory in feasible flight legs,
including straight and level flight, straight climbs and descents,
level turns, and climbing/descending turns. The final result is a
concisely described 4DT consisting of feasible flight segments.
The MOTO algorithm implemented for TMA operations
accounting for the combined objective is represented in Fig. 1.
MOTO-4D
Initial
Conditions
{0, 0, z0,
t0 , m0, v0,
0, 0, 0}
GFS Weather Field
{vw, p, RH, T}
(, , z, t)
Combined
Objective
4DT Generation
Global orthogonal
collocation
{f, f, zf,
tf , f ,
f, f}
Terminal
Conditions
Mathematically Optimal 4DT (CPWS)
OPERATIONAL
SMOOTHING
Straight
& Level
Model
Manoeuvre Identification
Operational
Transition
Criteria
Likelihood
Level
Turn
Model
Feasible 4DT Optimiser
Straight Straight Turning
Climb Descent Climb
Model
Model
Model
Turning
Descent
Model
Final Solution: Optimal and Feasible 4DT
Fig. 1. Block diagram of the MOTO-4D algorithm for TMA operations.
The currently employed solution technique is based on
direct solution methods of the global orthogonal collocation
family (pseudospectral), which are arguably the most effective
solution methods currently available [7]. Additionally, to
further enhance the algorithm stability and convergence
performances, path constraints and boundary conditions are
automatically strengthened on all state and control variables to
restrict the search domain as much as feasible.The TMA
MOTO algorithm considers the controlled time of arrival target
defined by Arrival Management (AMAN) functionalities. This
is used as the final time constraint by the TMA MOTO
algorithm. The Estimated Time of Arrival (ETA) may be
computed at multiple fixes along the flight path.
A. Operational Smoothing
The 4DT intent data produced as output of the operational
4DT smoothing include 4D waypoints (latitude, longitude,
altitude and time), fly-by/fly-over turn parameters, as well as
performance criteria and restrictions. Based on the manoeuvre
identification algorithm assessment on the CPWS trajectory,
the lateral path is constructed in terms of segments (straight and
turns), whose geometric characteristics depend on the required
course change
and the predicted Ground Speed (GS) of the
aircraft during turns. The ATM DSS computes turn radius R
and True Air Speed based on the selected altitude and takes
into account the predicted wind speed
at that altitude.
The bank angle is selected based on the aircraft dynamics
database, on the current altitude and on local/airline operational
restrictions. The radius R of a turn can be conservatively
calculated based on the maximum GS during the turn as:
(22)
where g is the gravity acceleration module. The turn arc
length is simply given by:
(23)
where the track change
is the difference in radians
between the final and the initial ground tracks. The following
prescriptions on , , lead distance (from the turn initiation
to the 4DT waypoint) and abeam distance (between the 4DT
waypoint and the point of the circular arc abeam it) are
implemented to plan fixed-radius fly-by turns that comply with
RTCA DO-229D, DO-236C and DO-283B [22, 23]:
1.
The fly-by transition is defined by equation (22)
combined with the following equations [22]:
(24)
(
)
(28)
(29)
3.
When transitioning from one airway to another, if both
require a FRT at the common waypoint, the smaller of the
two radii applicable shall be selected. In the case one of
the two airways does not involve a FRT, the FRT of the
other shall be implemented.
The criteria for implementing the definitions above in
different flight phases and at various altitudes are given in
Table 2-7 of RTCA DO-229D [23].
V. MOTO FOR ENROUTE OPERATIONS
Even neglecting current operational restrictions, the enroute
context typically involves limited or no variations in cruise
altitude. Additionally, the route distance and time involved are
substantially greater than the transients associated with the
aircraft flight dynamics model, which can therefore be
neglected since the entire phase takes place in quasi-steadystate conditions. As a result, optimal control based techniques
have limited or no advantages in this context. As a result, the
aviation research community is investigating alternative
formulations typically based on geometric steady-state
trajectory approximations. The mainstream philosophies
pursued involve either the generation of a grid of discrete 2D
points across the cruise space and the implementation of
suitable tree search algorithms to identify the optimal path, or
the implementation of evolutionary algorithms or more general
NLP solvers to optimise a discrete geometric trajectory.
Predefined flight level (FL) conditions are typically considered
to reduce the number of optimisation parameters and also to
meet current operational paradigms that prescribe semi-circular
cruise levels. The MOTO algorithm implemented for enroute
operations adopts a polygonal trajectory model, simplified
aircraft performance equations based on the steady-state
approximation of eq. 13-21 and NLP solvers. The variables
considered include the latitude and longitude of each and every
trajectory waypoint, the airspeed flown (which dictates the
throttle setting and therefore the pollutant emissions according
to the steady-state approximation of equations 13-21.
VI. TMA MOTO CASE STUDY
|⃗
⃗
|
{
(25)
{
2.
(26)
{
(27)
The geometry of the Fixed Radius Transition (FRT) is
defined by the track change
and the radius . The Lead
Distance and the Abeam Distance are defined based
on the radius and the track change as per the following
equations:
The sequencing of dense arrival traffic towards a single
final approach path was extensively assumed as a
representative case study of online tactical TMA operations.
The results of one exemplary simulation run are depicted in
Fig. 2. The TMA MOTO considers the selected arrival
sequence. Longitudinal separation is enforced at the mergepoint to ensure safe separation upon landing based on waketurbulence category and to prevent separation infringements in
the approach phase itself. The assumed minimum longitudinal
separation is 4 nautical miles on the approach path for medium
category aircraft approaching at 140 knots, therefore the
generated time slots are characterized by a 90~160 seconds
separation depending on the wake-turbulence categories of two
consecutive traffics. Fig. 3 depicts the computed 4DT in the
AMAN schedule display format. Waypoints and lines depicted
in magenta represent the flyable and concisely-described 4DT
consisting of a limited number of fly-by and overfly 4D
waypoints, obtained through the smoothing algorithm.
Fig. 2. Results of the TMA MOTO exploiting the operational 4DT
smoothing.
requirements for tactical online data-link negotiation of the
4DT in the TMA context.
VII. CONCLUSIONS
This paper presented two Multi-Objective Trajectory
Optimisation (MOTO) algorithms being developed for state-ofthe-art Air Traffic Management (ATM) Decision Support
Systems (DSS). These MOTO algorithms allow the replanning
of 4-Dimensional aircraft flight Trajectories (4DT) in strategic
and tactical online operations, in the presence of dense air
traffic, whenever airspace or air traffic reorganization is
required due to tactically changing airspace conditions as well
as promoting optimal rerouting around adverse weather.
Simulation case studies consistently support the viability of the
two proposed implementations: a fully-4D implementation for
TMA based on optimal control formulation and a 2Dimensional plus Time (2D+T) steady-state polygonal path
optimisation for the enroute context. In high air traffic density
conditions, the complete process of 4DT intent generation,
uplink and negotiation/validation can be consistently performed
within the 180 seconds limit. These results meet the 3 minutes
timeframe assumed for online tactical routing/rerouting tasks
and make the approach feasible for quasi-real-time
applications. The future algorithm evolutions will also
incorporate various Communication, Navigation, Surveillance,
ATM and Avionics (CNS+A) integrity monitoring and
augmentation strategies currently being researched [24, 25].
ACKNOWLEDGMENT
The authors gratefully acknowledge THALES Australia for
supporting the research presented in this article under RMIT
University Research Contract ID 0200312837.
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Fig. 3. Traffic sequencing represented in an AMAN scheduler plot.
Monte Carlo simulations resulted in an average of 11
seconds for single newly generated 4DT intents, and
consistently less than 30 seconds. The 4DT post-processing
allowed to reduce discretised CPWS trajectories of up to 450
points into a number of fly-by and overfly 4D waypoints
consistently below 20. These results meet the set design
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