一类非线性多变量系统的多模型自适应控制
Multiple model adaptive control for a class of nonlinear multivariable systems
摘要点击 50  全文点击 44  投稿时间:2018-02-03  修订日期:2019-09-25
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DOI编号  10.7641/CTA.2019.80102
  2020,37(4):829-836
中文关键词  非线性系统  多变量  神经网络  多模型自适应控制  高阶差分算子
英文关键词  nonlinear systems  multivariable  neural networks  multiple model adaptive control  high-order difference operator
基金项目  国家自然科学基金重大项目(61590922),国家自然科学基金青年项目(61503138),国家自然科学基金项目(61673268),中央高校基本科研业务费专项资金,上海市自然科学基金项目(16ZR1407300)
学科分类代码  
作者单位E-mail
黄帅 华东理工大学 1247666082@qq.com 
王昕 上海交通大学  
王振雷 华东理工大学 wangzhen_l@ecust.edu.cn 
中文摘要
      针对一类不确定的非线性多变量离散时间动态系统,提出了一种基于切换的多模型自适应控制方法。该控制方法的特点在于以下两个方面:首先,引入一个高阶差分算子使得非线性系统的非线性项的限制条件不再要求全局有界;其次,提出的控制方法由线性自适应控制器、神经网络非线性自适应控制器以及切换机构组成:线性控制器用来保证闭环系统的输入输出信号有界,神经网络非线性控制器用来改善闭环系统的性能,基于性能指标的切换机构在每一时刻选择性能指标较好的控制器对系统进行控制。理论分析和仿真实验说明了提出的多模型自适应控制方法的有效性。
英文摘要
      A multiple model adaptive control method based on switching is proposed for a class of uncertain nonlinear multivariable discrete-time dynamical systems. The control method is characterized by the following two aspects. Firstly, the restriction of the nonlinear terms of the nonlinear systems is not required to be global-bounded by introducing the high-order difference operator. Secondly, the proposed control method is composed of a linear adaptive controller, a neural network based nonlinear adaptive controller and a switching mechanism: the linear controller can ensure the boundness of the input and output signals of the closed loop system, the neural network nonlinear controller can improve the performance of the closed loop system, and the performance-based switching mechanism selects the controller which has the better performance to control system at any moment. Theoretic analysis and simulation experiments are presented to show the effectiveness of the proposed method.