A Geno-Fuzzy Fast Attitude Controller for Satellites Stabilized by Reaction WheelsA Geno-Fuzzy Fast Attitude Controller for Satellites Stabilized by Reaction Wheels
Fuzzy-based controller has many parameters that govern its performance which complicate the design process of this type of controllers. In this paper, a systematic procedure is proposed to design an optimal fuzzy logic controllers (FLC) for dynamical systems by the method of genetic algorithms. Then, this procedure is implemented to design a control system for three-axis satellites stabilized by reaction wheels. The parameters of FLC which include the rules and the distributions of membership functions are determined based on solving an optimization problem by minimizing a performance index. To get accurate pointing, longlife time and fast response for the satellite, the performance index includes the deviation of the satellite from its nominal position, the consumed power and the time of deviation. The simulations results show that the proposed technique was able to determine all the FLC parameters and generate a controller with a satisfactory performance.
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 A. Bryson, Control of Spacecraft and Aircraft (Princeton University Press, 1994).
 K. Boonlong, N. Chaiyaratana, S. Kuntanapreeda, Time Optimal and Time-Energy Optimal Control of a Satellite Attitude Using Genetic Algorithm, The 2002 ASME International Mechanical Engineering Congress and Exposition, New Orleans, LA (2002).
 M. Jamshidi, N. Vadiee, T. Ross, Fuzzy Logic and Control: Software and Hardware Applications (Prentice Hall, 1993).
 S. Daley, K. F. Gill, Attitude Control of A Spacecraft Using an Extended Self-Organizing Fuzzy Logic Controller, Proceedings of the Institution of Mechanical Engineering Science, vol. 201, n. 2, pp. 97-106 (1987).
 I. Dixon, G. Szego, Numerical Optimization of Dynamic Systems (North-Holland Co., 1980).
 D. Goldberg, Genetic Algorithm in Search, Optimization and Machine Learning (Addison-Wesley Longman Publishing Co., Inc. Boston, MA, USA, 1989).
 C. Coello Coello, Theoretical and Numerical Constraint-Handling Techniques Used with Evolutionary Algorithms: a Survey of the State of the Art, Computer Methods in Applied Mechanics and Engineering, vol. 191, n. 11-12, pp. 1245-1287, 2002.
 K. A. DeJong, Analysis of the Behavior of a Class of Genetic Adaptive Systems, Ph.D. Dissertation, University of Michigan, 1975.
 J. Grefenstette, Optimization of Control Parameters for Genetic Algorithms, Systems, Man and Cybernetics, IEEE Transactions on, vol. 16, n. 1, pp. 122-128, 1986.
 A. Chipperfield, P. Fleming, H. Polheim, C. Fonseca, Genetic Algorithm Toolbox User’s Guide, ACSE Research Report, 1994.
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