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Prescribed Performance Neural Control of Strict-Feedback Systems via Disturbance Observers

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  • Wei Xiang
  • Guangkui Xu
  • Fang Zhu
  • Chunzhi Yang

Abstract

This paper provides a disturbance observer-based prescribed performance control method for uncertain strict-feedback systems. To guarantee that the tracking error meets a design prescribed performance boundary (PPB) condition, an improved prescribed performance function is introduced. And radial basis function neural networks (RBFNNs) are used to approximate nonlinear functions, while second-order filters are employed to eliminate the “explosion-complexity” problem inherent in the existing method. Meanwhile, disturbance observers are constructed to estimate the compounded disturbance which includes time-varying disturbances and network construction errors. The stability of the whole closed-loop system is guaranteed via Lyapunov theory. Finally, comparative simulation results confirm that the proposed control method can achieve better tracking performance.

Suggested Citation

  • Wei Xiang & Guangkui Xu & Fang Zhu & Chunzhi Yang, 2020. "Prescribed Performance Neural Control of Strict-Feedback Systems via Disturbance Observers," Complexity, Hindawi, vol. 2020, pages 1-12, October.
    • Handle: RePEc:hin:complx:8835512
    DOI: 10.1155/2020/8835512
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