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Self-Evolving Chebyshev Radial Basis Function Neural Complementary Sliding Mode Control

Author

Listed:
  • Lei Zhang

    (College of Information Science and Engineering, Hohai University, Changzhou 213022, China)

  • Xiangguo Li

    (College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, China)

  • Juntao Fei

    (College of Information Science and Engineering, Hohai University, Changzhou 213022, China
    College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, China
    Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology, College of Artificial Intelligence and Automation, Hohai University, Changzhou 213022, China)

Abstract

A novel intelligent complementary sliding mode control (ICSMC) method is proposed for nonlinear systems with unknown uncertainties in this paper. A self-evolving Chebyshev radial basis function neural network (RBFNN) (SECRBFNN) with self-learning parameters and structure is proposed and combined with complementary sliding mode control (CSMC). CSMC not only has the advantages of the strong robustness of traditional SMC but also has certain advantages in reducing chattering and control accuracy. The SECRBFNN, which combines the advantages of the Chebyshev network (CN) and an RBFNN, is used to estimate unknown uncertainties in nonlinear systems. Meanwhile, a node self-evolution mechanism is proposed to avoid redundancy in the number of neurons. Eventually, the detailed simulation results demonstrate the feasibility and superiority of the proposed method.

Suggested Citation

  • Lei Zhang & Xiangguo Li & Juntao Fei, 2023. "Self-Evolving Chebyshev Radial Basis Function Neural Complementary Sliding Mode Control," Mathematics, MDPI, vol. 11(14), pages 1-18, July.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:14:p:3231-:d:1200187
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    References listed on IDEAS

    as
    1. Anh Tuan Vo & Thanh Nguyen Truong & Hee-Jun Kang, 2023. "Fixed-Time RBFNN-Based Prescribed Performance Control for Robot Manipulators: Achieving Global Convergence and Control Performance Improvement," Mathematics, MDPI, vol. 11(10), pages 1-25, May.
    2. Jiacheng Wang & Yunmei Fang & Juntao Fei, 2023. "Adaptive Super-Twisting Sliding Mode Control of Active Power Filter Using Interval Type-2-Fuzzy Neural Networks," Mathematics, MDPI, vol. 11(12), pages 1-20, June.
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