期刊
MATHEMATICS
卷 10, 期 7, 页码 -出版社
MDPI
DOI: 10.3390/math10071182
关键词
sliding mode control; neural network (NN); SISO nonlinear systems; Lyapunov stability theory
类别
资金
- National Natural Science Foundation of China [62073045,61973185]
- Natural Science Foundation of Shandong Province [ZR2020MF097]
- Development Plan of Young Innovation Team in Colleges and Universities of Shandong Province [2019KJN011]
- Colleges and Universities Twenty Terms Foundation of Jinan City [2021GXRC100]
This article proposes a sliding mode control method based on an adaptive neural network for handling nonlinear systems with unknown dynamic functions. By introducing the boundary layer technique and continuous proportional function, the chattering phenomenon caused by discontinuous switching terms is effectively alleviated.
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.
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