Abstract
Conventional adaptive control techniques have, for the most part, been based on methods for linear or weakly non-linear systems. More recently, neural network and genetic algorithm controllers have started to be applied to complex, non-linear dynamic systems. The control of chaotic dynamic systems poses a series of especially challenging problems. In this paper, an adaptive control architecture using neural networks and genetic algorithms is applied to a complex, highly nonlinear, chaotic dynamic system: the adaptive attitude control problem (for a satellite), in the presence of large, external forces (which left to themselves led the system into a chaotic motion). In contrast to the OGY method, which uses small control adjustments to stabilize a chaotic system in an otherwise unstable but natural periodic orbit of the system, the neuro-genetic controller may use large control adjustments and proves capable of effectively attaining any specified system state, with no a prioriknowledge of the dynamics, even in the presence of significant noise.
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References
Dracopoulos DC, Jones AJ. Neuromodels of analytic dynamic systems. Neural Comput & Applic 1993; 1(4): 268–279
Dracopoulos DC, Jones AJ. Neuro-genetic adaptive attitude control. Neural Comput & Applic 1994; 2(4): 183–204
Miller T, Sutton RS, Werbos PJ, Eds. Neural Networks for Control. MIT Press, 1990
Astrom KJ. Towards intelligent control. IEEE Control Systems Magazine April 1988
Goldberg KY, Pearlmutter BA. Using backpropagation with temporal windows to learn the dynamics of the cmu direct drive arm II. Neural Information Processing Systems 1. Morgan Kaufmann, 1989
Landau YD. Adaptive Control: The Model Reference Approach. Marcel Dekker, 1979
Parks PC. Lyapunov redesign of model reference adaptive control systems. IEEE Trans Automatic Control 1966; 11: 362–367
Narendra KS, Annaswamy AM. Stable Adaptive Systems. Prentice Hall, 1989
Astrom KJ, Wittenmark B. Adaptive Control. Addison-Wesley, 1989
Qammar HK, Mossayebi F. System identification and model-based control of a chaotic system. Int J Bifurcation and Chaos 1994; 4: 843–851
Fradkov AL, Pogromsky A, Markov A. Adaptive control of chaotic continuous-time systems. Proc Third Euro Control Conf, Rome, 1995, pp. 3062–3067
Ryan EP. A universal adaptive stabilizer for a class of nonlinear systems. Systems and Control Lett 1991; 16: 209–218
Ilchmann A, Ryan EP. Universal λ-tracking for nonlinearity-perturbed systems in the presence of noise. Automatica 1994; 30(2): 337–346
Narendra KS, Parthasarathy K. Identification and control of dynamical systems using neural networks. Neural Networks 1990; 1(1): 4–27
Narendra KS, Mukhopadhyay S. Intelligent control using neural networks. IEEE Control Systems Mag 1992: 11–18
Narendra KS. Adaptive control of dynamical systems using neural networks. In: Handbook of Intelligent Control, DA White, DA Sofge, Eds. Van Nostrand Reinhold, 1992
Rajarshi R, Murphy Jr TW, Maier TD, Gills Z, Hunt ER. Dynamical control of a chaotic laser: Experimental stabilisation of a globally coupled system. Phys Rev Lett 1992; 68(9): 1259–1262
Ott E, Grebogi C, Yorke J. Controlling chaos. Phys Rev Lett 1990; 64(11)
Jordan MI, Rumelhart DE. Forward models: Supervised learning with a distal teacher. Cognitive Sci 1992; 16: 307–354
Rumelhart D, McClelland J. PDP research group. Parallel Distributed Processing — Explorations in the Microstructure Cognition, vol. 1. MIT Press, 1986
Werbos PJ. Backpropagation through time: What it does and how to do it. Proc IEEE 1990; 78(10)
Kawato M. Computational schemes and neural network models for formation and control of multijoint arm trajectory. In: Neural Networks for Control, S Miller, DJ Werbos, Eds. MIT Press, 1990
Kawato M, Uno T, Isobe M, Suzuki R. Hierarchical neural network model for voluntary movement with application. IEEE Control Systems Mag 1988
Katayama M, Kawato M. Learning trajectory and force control of an artificial muscle arm by parallel-hierarchical neural network model. Neural Information Processing Systems 3. Morgan Kaufmann, 1991
Psaltis D, Sideris A, Yamamura AA. A multilayered neural network controller. IEEE Control Systems Mag 1988: 17–21
Dracopoulos DC. Neuromodelling, Adaptive Neurocontrol and the Attitude Control Problem. PhD thesis, Imperial College of Science, Technology and Medicine, University of London, March 1994
Chen G, Dong X. From chaos to order — perspectives and methodologies in controlling chaotic nonlinear dynamical systems. Int J Bifurcation and Chaos 1993; 3(6): 1363–1409
Ditto WL, Rauseo SN, Spano ML. Experimental control of chaos. Phys Rev Lett 1990; 65(26)
Meyer G. Design and global analysis of spacecraft attitude control systems. Technical Report TR R-361, NASA, 1971
Leipnik RB, Newton TA. Double strange attractors in rigid body motion with linear feedback control. Physics Lett 1981; 86A: 63–67
Lapedes A, Farber R. How neural nets work. Proc IEEE Denver Conf Neural Nets, 1987
Werbos P. Neurocontrol and related techniques. In: Handbook of Neural Computing Applications, A Maren Ed. Academic Press, 1990, pp. 345–381
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This work was partly supported by SERC grant 90800355.
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Dracopoulos, D.C., Jones, A.J. Adaptive neuro-genetic control of chaos applied to the attitude control problem. Neural Comput & Applic 6, 102–115 (1997). https://doi.org/10.1007/BF01414007
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DOI: https://doi.org/10.1007/BF01414007