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多变量非线性系统的变阶采样迭代学习控制

DOI: 10.3724/SP.J.1004.2013.01027, PP. 1027-1036

Keywords: 多变量系统,采样系统,初始修正作用,变阶迭代学习控制

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Abstract:

?针对存在初态误差的情形,提出多变量非线性系统的变阶采样迭代学习控制方法.相对固定阶迭代学习算法,变阶算法可有效降低跟踪误差.对变阶采样迭代学习算法进行了收敛性分析,推导出收敛充分条件.给出了变阶学习的两种实现策略-DD(Directdivision)和DIP(Divisioninphases)策略.数值仿真表明,基于DIP策略的变阶采样迭代学习算法在获得较高的控制精度的同时,具有较快的收敛速度.

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