Abstract
A new robust controller based on linear-paremeter-neural-networks is designed for a class of nonlinear unkonwn chaotic systems which could be turned to “standard block control type” by using backstepping method. It was proved by constructing Lyapunov function step by step that all signals of the system are bounded and exponentially converge to the neighborhood of the origin globally and the weights of neural network converge to the optimal weights eventually. The assumption for unknown control function is reduced which stand for the innovation of our method compared with the traditional method. Also the unknown control function needn’t to be positive or negative strictly in our paper. This assumption in the other papers is so strict that it couldn’t be satisfied by many practical systems. So our method can be applied to a more extensive nonlinear systems. At last, take the unknown Duffing chaotic system for example, simulation study is given to demonstrate that the proposed method is effective.
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References
Ungar, L.H., Powell, B.A., Kamens, S.N.: Adaptive networks for fault diagnosis and process control. Computers & Chemical Engineering 14, 561–572 (1990)
Chen, F.C., Liu, C.C.: Adaptively controlling nonlinear continuous-time systems using multiplayer neural networks. IEEE Transactions on Automatic Control 39, 1306–1310 (1994)
Polycarpou, M.M., Ioannou, P.A.: A robust adaptive nonlinear control design. Automatica 32(3), 423–427 (1996)
Kwan, C., Lewis, F.L.: Robust backstepping control of nonlinear systems using neural networks. IEEE Transactions on systems, man and cybernetics 30(6), 753–766 (2000)
Kim, B.S., Calise, A.J.: Nonlinear Flight Control Using Neural Networks. In: Proc. of rhe AZAA Guidance, Navigation and Coy2trol Conference, Scottsdale, AZ, pp. 930–940 (1994)
Leitner, J.: Helicopter Nonlinear Control Using Adaptive Feedback Linearization, Ph.D. Thesis, Georgia Institute of Technology, Atlanta, CA (1995)
McFarland, M.B., Calise, A.J.: Neural Networks for Stable Adaptive Control of Air-to-Air Missiles. In: Proc. Of the AIM Guidance, Navigation and Control Conference, Baltimore, MD (1995)
Serakos, D.: Nonlinear Controllers for a Tail-Fin Controlled Missile. In: Proc. IEEE Southeastcon, Charlotte, NC 1993 (2000)
Qu, Z.: Robust Control of Nonlinear Uncertain Systems Under Generalized Matching Conditions. Automatica 29, 985–998 (1993)
Marino, R., Tomei, P.: Robust Stabilization of Feedback Linearizable Time-varying Uncertain Nonlinear Systems. Automatica 29, 181–189 (1993)
Kanellakopoulos, I., Kokotovic, P.V., Marino, R.: An Extended Direct Scheme for Robust Adaptive Nonlinear Control. Automatica 27, 247–255 (1991); Automation (ICCA 2004)
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© 2006 Springer-Verlag Berlin Heidelberg
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Wang, X., Wang, H., Li, H., Lei, J. (2006). Stable Robust Control for Chaotic Systems Based on Linear-Paremeter-Neural-Networks. In: Jiao, L., Wang, L., Gao, Xb., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881070_30
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DOI: https://doi.org/10.1007/11881070_30
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