Neural network based adaptive sliding mode control of uncertain nonlinear systems

查看全文 作　　者：Ghania [1]Debbache;Noureddine [1]Goléa

机构地区：[1]Electrical Engineering Institute, Oum El Bouaghi University, 04000 Oum El Bouaghi, Algeria

出　　处：《Journal of Systems Engineering and Electronics》索引2012年第23卷第1期,共10页

摘　　要：The purpose of this paper is the design of neural network-based adaptive sliding mode controller for uncertain unknown nonlinear systems. A special architecture adaptive neural network, with hyperbolic tangent activation functions, is used to emulate the equivalent and switching control terms of the classic sliding mode control (SMC). Lyapunov stability theory is used to guarantee a uniform ultimate boundedness property for the tracking error, as well as of all other signals in the closed loop. In addition to keeping the stability and robustness properties of the SMC, the neural network-based adaptive sliding mode controller exhibits perfect rejection of faults arising during the system operating. Simulation studies are used to illustrate and clarify the theoretical results. 关键词：自适应神经网络不确定非线性系统滑模控制器 LYAPUNOV稳定性理论滑动模式未知非线性系统自适应滑模控制最终有界性

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Neural network based adaptive sliding mode control of uncertain nonlinear systems

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