Kouamana Bousson
Associate Professor of Aerospace Sciences
address
Aerospace Sciences Department
Calçada Fonte do Lameiro
6201-001 Covilhã, Portugal
email
bousson@ubi.pt, k1bousson@yahoo.com
phone
+351 275 329 713
fax
+351 275 329 768
Research Interests
Aircraft Control Systems, Trajectory Optimization & Control, Identification
Teaching Interests
Aircraft Systems, Flight Dynamics & Control, Missile Guidance & Control
Education
PhD, National Institute of Applied Sciences, Paul Sabatier University, Toulouse, 1993
MSc, Paul Sabatier University, Toulouse, 1989
MEng, Ecole Nationale de l’Aviation Civile (ENAC), Toulouse, 1988
Current and Previous Positions
Associate Professor, Aerospace Sciences Dept., University of Beira Interior, 2004-present
Course Director, Aerospace Sciences Dept., University of Beira Interior, 2001-2006
Vice-President, Aerospace Sciences Dept., University of Beira Interior, 2002-2005
Assistant Professor, Aerospace Sciences Dept., University of Beira Interior, 1998-2004
Invited Assistant Professor, Aerospace Sciences Dept., Univ. of Beira Interior, 1995-1998
Researcher, French National Council for Scientific Research (CNRS), Toulouse, 1993-1995
Teaching Associate, Ecole Nationale de l’Aviation Civile (ENAC), Toulouse, 1992-1994
Teaching Assistant, Ecole Supérieure d’Informatique et de Gestion, Toulouse, 1990-1992
Society Membership
AIAA-American Institute of Aeronautics and Astronautics
Publications
Author or co-author of more than 50 publications (books and papers in journals and
conference proceedings)
Some Relevant Publications
Bousson, K., “Time-varying Parameter Estimation with Application to Trajectory Tracking”,
accepted for publication in Aircraft Engineering and Aerospace Technology, Vol. 79(4),
2007.
Abstract: This paper is concerned with an online parameter estimation algorithm for
nonlinear uncertain time-varying systems for which no stochastic information is available.
The estimation procedure, called Nonlinear Learning Rate Adaptation (NLRA), computes an
individual adaptive learning rate for each parameter instead of using a single adaptive learning
rate for all the parameters as done in stochastic approximation, each individual learning rate
being controlled by a meta-learning rate rule for the sake of minimizing the measurement
prediction error. The method does not require stochastic information about the system model
and the measurement noise covariance matrices contrarily to the Kalman filtering. Numerical
results about aircraft navigation trajectory tracking show that the method is able to estimate
reliably time-varying parameters even in presence of measurement noise.
Bousson, K. and Correia, S.D., “Optimization Algorithm Based on Densification and
Dynamic Canonical Descent”, Journal of Computational and Applied Mathematics, Vol. 191,
pp. 269-279, 2006.
Abstract: Stochastic methods have gained some popularity in global optimization in that
most of them do not assume the cost functions to be differentiable. They have capabilities to
avoid being trapped by local optima, and may converge even faster than gradient-based
optimization methods on some problems. The present paper proposes an optimization method,
which reduces the search space by means of densification curves, coupled with the dynamic
canonical descent algorithm. The performances of the new method are shown on several
known problems classically used for testing optimization algorithms, and proved to
outperform competitive algorithms such as simulated annealing and genetic algorithms.
Bousson, K., “Single Grid-point Dynamic Programming for Trajectory Optimization”, AIAA
Atmospheric Flight Mechanics Conference and Exhibit, Paper Nº AIAA 2005-5902, San
Francisco, USA, 15-18 August 2005.
Abstract: A Dynamic Programming (DP) method is presented in the framework of trajectory
optimization. The method is based on the use of a single state grid-point at each stage, and on
the successive reductions of the value of the cost-to-go at each iteration up to convergence.
The method has the advantage of requiring less computation memory and time than Iterative
Dynamic Programming that has been the best extension to dynamic programming known so
far. The main contribution of the proposed method is to provide an efficient dynamic
programming method for high dimension systems and for both classes of smooth and
nonsmooth cost functions. Numerical examples are presented, and the results are shown to be
much better than those obtained through Iterative Dynamic Programming.
Bousson, K., “Viable Feedback Space Trajectory Control”, Proceedings of the International
Council for Astronautical Sciences (ICAS-2004), Paper ID-462, Yokohama, Japan, August 29
– September 3, 2004.
Abstract: The present paper proposes a method for controlling an aerial vehicle inside a
corridor without leaving it, the dynamics of the vehicle being subject to bound constraints on
the control variables. Such a control method is necessary when the system under control does
not need to follow exactly a curvilinear reference trajectory. Therefore, what is in fact
required from such a system is to maintain the trajectory close to some nominal reference
within a certain admissible tolerance. Based on the known initial and terminal positions of the
system, a sequence of waypoints is chosen in such a way that the segment joining two
consecutive waypoints remain fully inside the control corridor. Then a predictive control law
is designed to control the system from a waypoint to the next until the specified terminal
position is reached. An application dealing with orbital control illustrates the method and
reveals its potentials for handling the control of complex systems even in case of unknown
measurement noise.
K. Bousson: “Sequential Nonlinear Identification of Stability Derivatives by Gain
Adaptation”, Russian-American Journal of Actual Problems of Aviation and Aerospace
Systems, Vol. 7, No.2(14), pp. 61-72, 2002.
Abstract: The identification of stability derivatives is the backbone of adaptive flight control
and airborne simulation systems mainly in the case when these derivatives are subject to
changes. The present paper proposes an on-line stability derivative identification method for
nonlinear flight models. The identification procedure computes an individual adaptive
learning rate for each stability derivative instead of using a single adaptive learning rate for all
the stability derivatives, each individual learning rate depending on a meta-learning rate. The
method is on the one hand void of probabilistic information about the flight model and the
measurement errors contrary to the Kalman filtering, and on the other hand it guarantees fast
convergence to optimal values. The convergence properties of the method are stated and
proved, and the practical efficiency successfully demonstrated on an aircraft longitudinal
model.
K. Bousson: “Sequential Parameter Identification Method For Nonlinear Systems”,
Proceedings of the IEEE International Symposium on Intelligent Control, Vancouver,
Canada, October 27-30, pp. 874-878, 2002.
Abstract: In this paper we propose an accurate online parameter identification algorithm for
nonlinear time-varying systems. For such systems, techniques based on cross-validation to
achieve regularization or model selection are not possible, and classical least square
techniques are not reliable mainly when the dynamics of the system is highly nonlinear. To
overcome these problems, an identification algorithm devised from Sutton’s dynamic learning
rate techniques and based on a learning window and forgetting factor criterion has been used.
In doing so, the proposed algorithm avoids the need for heuristic choices of the initial
conditions and noise covariance matrices required by the Kalman filtering. The performance
of the proposed method is demonstrated in the end on aircraft flight dynamics parameters
identification in the horizontal plane.
K. Bousson: “Automatic Guidance of Aircraft for Collision Avoidance in Terminal Areas”,
Russian-American Journal of Actual Problems of Aviation and Aerospace Systems, Vol. 6,
No.2(12), pp. 49-57, 2001.
Abstract: The present paper copes with aircraft optimal guidance for automatic collision
avoidance in terminal areas. The collision avoidance problem is expressed as an optimization
problem whose solution vector is composed of individual aircraft headings, velocities and
flight levels as guidance information that corresponding aircraft should follow to
automatically avoid collision and at the same time to converge to a specified landing
procedure start point. Simulation results are presented and show that the proposed method is
capable of ensuring collision avoidance in an optimal way.
K. Bousson: “Efficient Global Optimization Based on Dynamic Canonical Descent”. Journal
Systems Science, Vol. 26, Nº 4, pp. 61-78, 2001.
Abstract: Stochastic methods have gained some popularity in global optimization in that
most of them do not assume the cost functions to be differentiable, have capabilities to avoid
being trapped by local optima, and may converge even faster than gradient-based optimization
methods on some problems. The present paper briefly reviews the advantages and the
limitations of some classical stochastic optimization algorithms such as genetic algorithms
(GA) and simulated annealing (SA), and then proposes a faster derivative-free and
deterministic (non-stochastic) global optimization algorithm which retains their advantages
while avoiding their disadvantages.
F. Guerrin, K. Bousson, J.Ph. Steyer, L. Travé-Massuyès: “Qualitative Reasoning Methods
for CELSS Modeling”, Advances in Space Research Journal, Vol. 14, Nº. 11, pp. (11)307 (11)312, 1994.
Abstract: Qualitative Reasoning (QR) is a branch of Artificial Intelligence that arose from
research on engineering problem solving. This paper describes the major QR methods and
techniques, which, we believe, are capable of addressing some of the problems that are
emphasized in the literature and posed by CELSS modeling, simulation, and control at the
supervisory level.