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Neural network-based robust adaptive control of nonlinear systems with unmodeled dynamics

Author

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  • Wang, Dan
  • Huang, Jialiang
  • Lan, Weiyao
  • Li, Xiaoqiang

Abstract

A neural network-based robust adaptive control design scheme is developed for a class of nonlinear systems represented by input–output models with an unknown nonlinear function and unmodeled dynamics. By on-line approximating the unknown nonlinear functions and unmodeled dynamics by radial basis function (RBF) networks, the proposed approach does not require the unknown parameters to satisfy the linear dependence condition. It is proved that with the proposed control law, the closed-loop system is stable and the tracking error converges to zero in the presence of unmodeled dynamics and unknown nonlinearity. A simulation example is presented to demonstrate the method.

Suggested Citation

  • Wang, Dan & Huang, Jialiang & Lan, Weiyao & Li, Xiaoqiang, 2009. "Neural network-based robust adaptive control of nonlinear systems with unmodeled dynamics," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(5), pages 1745-1753.
    • Handle: RePEc:eee:matcom:v:79:y:2009:i:5:p:1745-1753
    DOI: 10.1016/j.matcom.2008.09.002
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    Cited by:

    1. Zhang, Yu & Feng, Zhi Guo & Yang, Xinsong & Alsaadi, Fuad E. & Ahmad, Bashir, 2018. "Finite-time stabilization for a class of nonlinear systems via optimal control," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 146(C), pages 14-26.
    2. Hollweg, Guilherme Vieira & Evald, Paulo Jefferson Dias de Oliveira & Milbradt, Deise Maria Cirolini & Tambara, Rodrigo Varella & Gründling, Hilton Abílio, 2022. "Design of continuous-time model reference adaptive and super-twisting sliding mode controller," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 201(C), pages 215-238.

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