Neural-network-based robust adaptive control for a class of nonlinear systems |
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Authors: | Chih-Min Lin Ang-Bung Ting Ming-Chia Li Te-Yu Chen |
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Affiliation: | (1) Department of Electrical Engineering, Yuan Ze University, Chung-Li, Tao-Yuan 320, Taiwan, Republic of China;(2) Information and Communication Research Division, Chung-Shan Institute of Science and Technology, Long-Tan, Tao-Yuan, 325, Taiwan, Republic of China |
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Abstract: | In this study, a robust adaptive control (RAC) system is developed for a class of nonlinear systems. The RAC system is comprised
of a computation controller and a robust compensator. The computation controller containing a radial basis function (RBF)
neural network is the principal controller, and the robust compensator can provide the smooth and chattering-free stability
compensation. The RBF neural network is used to approximate the system dynamics, and the adaptive laws are derived to on-line
tune the parameters of the neural network so as to achieve favorable estimation performance. From the Lyapunov stability analysis,
it is shown that all signals in the closed-loop RBAC system are uniformly ultimately bounded. To investigate the effectiveness
of the RAC system, the design methodology is applied to control two nonlinear systems: a wing rock motion system and a Chua’s
chaotic circuit system. Simulation results demonstrate that the proposed RAC system can achieve favorable tracking performance
with unknown of the system dynamics. |
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