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Neural-network-based robust adaptive control for a class of nonlinear systems

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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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Acknowledgments

This work was supported partially by the National Science Council of the Republic of China under Grant NSC94-2213-E-155-010.

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Authors and Affiliations

  1. Department of Electrical Engineering, Yuan Ze University, Chung-Li, Tao-Yuan 320, Taiwan, Republic of China

    Chih-Min Lin, Ang-Bung Ting & Ming-Chia Li

  2. Information and Communication Research Division, Chung-Shan Institute of Science and Technology, Long-Tan, Tao-Yuan, 325, Taiwan, Republic of China

    Te-Yu Chen

Authors
  1. Chih-Min Lin
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  2. Ang-Bung Ting
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  3. Ming-Chia Li
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  4. Te-Yu Chen
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Correspondence to Chih-Min Lin.

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Lin, CM., Ting, AB., Li, MC. et al. Neural-network-based robust adaptive control for a class of nonlinear systems. Neural Comput & Applic 20, 557–563 (2011). https://doi.org/10.1007/s00521-011-0561-2

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  • DOI: https://doi.org/10.1007/s00521-011-0561-2

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