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

Lei Zhang, Xiangguo Li and Juntao Fei ()
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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

Mathematics, 2023, vol. 11, issue 14, 1-18

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.

Keywords: complementary sliding mode control (CSMC); self-evolving Chebyshev radial basis function neural network (SECRBFNN); node self-evolution mechanism; nonlinear systems control (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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