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

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 si...

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Bibliographic Details
Published inMathematics (Basel) Vol. 10; no. 7; p. 1182
Main Authors Li, Bin, Zhu, Jiahao, Zhou, Ranran, Wen, Guoxing
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.04.2022
Subjects
Adaptive control
Approximation
Boundary layers
Continuity (mathematics)
Control theory
Controllers
Design
Lyapunov stability theory
Mathematics
Methods
neural network (NN)
Neural networks
Nonlinear systems
Reference signals
SISO (control systems)
SISO nonlinear systems
Sliding mode control
Switching
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Summary: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.
ISSN:2227-7390
2227-7390
DOI:10.3390/math10071182