Robust output feedback controller for discrete-time nonlinear systems based on standard neural network model

Jianhai Zhang, Wanzeng Kong, Sanqing Hu
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Abstract

Neural networks and T-S fuzzy systems have been widely used in nonlinear system control. Standard neural network model (SNNM) can be used to describe intelligent systems composed of neural networks or T-S fuzzy models, and so provides a common controller synthesis framework for these kinds of systems. This paper investigates robust output feedback controller synthesis of discrete-time nonlinear systems based on SNNM. A new output feedback controller design technique for discrete-time SNNM in terms of linear matrix inequality is proposed. The aforementioned intelligent systems can be transformed into SNNM for controller synthesis in a unified way. The numerical example and simulation result show that the presented method is effective and provide a new approach to the nonlinear system controller synthesis.
基于标准神经网络模型的离散非线性系统鲁棒输出反馈控制器
神经网络和T-S模糊系统在非线性系统控制中有着广泛的应用。标准神经网络模型(SNNM)可以用来描述由神经网络或T-S模糊模型组成的智能系统,从而为这类系统提供了一个通用的控制器综合框架。研究了基于SNNM的离散非线性系统鲁棒输出反馈控制器的综合。提出了一种基于线性矩阵不等式的离散SNNM输出反馈控制器设计方法。上述智能系统可以统一转化为SNNM进行控制器合成。数值算例和仿真结果表明,该方法是有效的,为非线性系统控制器的综合提供了一种新的途径。
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