Multiple Model Adaptive Control for a Class of Nonlinear Multi-variable Systems with Zero-order Proximity Boundedness
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摘要: 针对一类多变量非线性离散时间系统,提出一种新的基于神经网络的多模型自适应控制方法.为了将非线性系统的高阶非线性项的限制条件放宽到零阶接近有界,该方法引入了一种新的非线性模型.该模型在传统线性回归模型基础上增加了非线性补偿项,使模型的估计误差有界.一个神经网络模型与非线性模型同时被用来对系统进行辨识.基于性能指标的切换机构选择性能较好的模型对应的控制器 对系统进行控制. 理论分析证明了零阶接近有界多模型自适应控制系统的有界输 入和有界输出稳定性. 仿真实验说明了提出的多模型自适应控制方法的有效性.Abstract: A novel multiple model adaptive control method using neural networks is proposed for a class of MIMO nonlinear discrete-time systems. In order to relax the restriction of the higher order nonlinear term of the nonlinear system to zeroorder proximity boundedness, this method introduces a new nonlinear model. The model adds a nonlinear compensation term to the conventional linear autoregressive model such that the estimation error is bounded. A neural network model is used to identify the system with nonlinear model simultaneously. A performance-based switching mechanism determines the controller which has the better performance to control the system. Theoretic analysis proves the bounded-input-boundedoutput stability of the zero-order proximity boundedness multiple model adaptive control system. Simulation results are presented to show the effectiveness of the proposed method.
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