Performance Analysis for Neuro Sliding Mode Control with Gain Adjustment

Chenghu Jing, Wang Wu, Mao Lin
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引用次数: 3

Abstract

Sliding mode control is a typical nonlinear control strategy which was easy realized and with strong robustness, in this paper, a neuro sliding mode controller was designed with RBF neural networks and the stability of the proposed control scheme is proved by Lyapnouv theorem. For the chattering of sliding mode control are often derive from switching gain, the gain was adjusted with neural networks with RBF networks’ output, the algorithm with fixed gain and adaptive gain are all proposed, also the control scheme is applied to a nonlinear system, simulation studies shows the methods is effective and can applied into nonlinear control system.
增益调节神经滑模控制的性能分析
滑模控制是一种典型的非线性控制策略,易于实现且具有较强的鲁棒性,本文采用RBF神经网络设计了一种神经滑模控制器,并用李亚普诺夫定理证明了所提控制方案的稳定性。针对滑模控制的抖振往往是由切换增益引起的问题,利用RBF网络输出的神经网络对增益进行调节,分别提出了固定增益和自适应增益的控制算法,并将该控制方案应用于非线性系统,仿真研究表明该方法是有效的,可以应用于非线性控制系统。
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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