付兴建;郭宏梅;

• 1:北京信息科技大学自动化学院

摘要(Abstract)：

针对不确定时滞系统的鲁棒跟踪控制问题,设计了一种基于小脑神经网络CMAC的鲁棒非脆弱控制器。首先,给出小脑模型神经网络控制系统的算法。其次针对一类不确定时滞系统,根据李雅普诺夫稳定理论,进行了鲁棒非脆弱控制器的设计。假设反馈控制中即含有状态反馈不确定性,也具有状态时滞的不确定性。证明不确定时滞系统鲁棒非脆弱控制存在的条件。该条件可以利用Matlab的线性矩阵不等式LMI工具箱来求解鲁棒控制器的参数。之后利用CMAC神经网络较强的学习能力和鲁棒非脆弱控制器对参数摄动抑制作用的特点,将鲁棒非脆弱控制器与小脑模型神经网络CMAC相结合,构成小脑模型神经网络与鲁棒非脆弱控制器的复合控制,实现对不确定时滞系统的跟踪控制。仿真结果显示,对于输入端扰动和一定程度的参数摄动,经过复合控制器的作用,被控系统能在短时间的抖动后逐渐趋于稳定,不仅具有较快的响应速度,还具有较短的收敛时间和令人满意的跟踪精度。该种复合控制表现出较强抗干扰能力及鲁棒性。

关键词(KeyWords)： 小脑模型神经网络;非脆弱控制;不确定性;时滞;线性矩阵不等式

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金(61573230);; 北京信息科技大学促进高校内涵发展科研水平提高项目(5211910950);; 北京市教委科技计划项目(KM201811232013)

作者(Author): 付兴建;郭宏梅;

Email:

DOI: 10.13800/j.cnki.xakjdxxb.2020.0314

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