Adaptive neural network fault-tolerant control for a class of nonlinear systems

Ke Qi
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引用次数: 1

Abstract

In this paper, a direct adaptive neural network sliding-mode fault-tolerance control architecture is proposed for a class of SISO nonlinear systems. The architecture employs neural network to approximate the optimal controller which is designed on the assumption that all the dynamics in the system are known. With the sliding-mode controller technique, the influence of the uncertainty on the systems was considerably reduced. Furthermore, Global asymptotic stability is established in the Lyapunov sense, with the tracking errors converging to a neighborhood of zero. The example shows that the proposed control architecture is effective for a class of SISO nonlinear system.
一类非线性系统的自适应神经网络容错控制
针对一类SISO非线性系统,提出了一种直接自适应神经网络滑模容错控制体系结构。该体系结构采用神经网络逼近最优控制器,该控制器是在系统的所有动力学都已知的前提下设计的。采用滑模控制技术,大大降低了不确定性对系统的影响。进一步,在Lyapunov意义下建立了系统的全局渐近稳定性,跟踪误差收敛到零的邻域。算例表明,所提出的控制体系对于一类SISO非线性系统是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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