Adaptive Neural Network Sliding Mode Control for a Class of SISO Nonlinear Systems

Bin Li, Jiahao Zhu, Ranran Zhou and Guoxing Wen
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Bin Li: School of Mathematics and Statistics, Qilu University of Technology, Shandong Academy of Sciences, Jinan 250353, China
Jiahao Zhu: School of Mathematics and Statistics, Qilu University of Technology, Shandong Academy of Sciences, Jinan 250353, China
Ranran Zhou: School of Mathematics and Statistics, Qilu University of Technology, Shandong Academy of Sciences, Jinan 250353, China
Guoxing Wen: School of Mathematics and Statistics, Qilu University of Technology, Shandong Academy of Sciences, Jinan 250353, China

Mathematics, 2022, vol. 10, issue 7, 1-12

Abstract: In this article, a sliding mode control (SMC) is proposed on the basis of an adaptive neural network (NN) for a class of Single-Input–Single-Output (SISO) nonlinear systems containing unknown dynamic functions. Since the control objective is to steer the system states to track the given reference signals, the SMC method is considered by employing the adaptive neural network (NN) strategy for dealing with the unknown dynamic problem. In order to compress the impaction coming from chattering phenomenon (which inherently exists in most SMC methods because of the discontinuous switching term), the boundary layer technique is considered. The basic design idea is to introduce a continuous proportional function to replace the discontinuous switching control term inside the boundary layer so that the chattering can be effectively alleviated. Finally, both Lyapunov theoretical analysis and computer numerical simulation are used to verify the effectiveness of the proposed SMC method.

Keywords: sliding mode control; neural network (NN); SISO nonlinear systems; Lyapunov stability theory (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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