迭代学习控制
趋同(经济学)
序列(生物学)
数学
控制理论(社会学)
不变(物理)
算法
LTI系统理论
离散时间和连续时间
线性系统
计算机科学
控制(管理)
人工智能
数学分析
遗传学
生物
统计
经济增长
经济
数学物理
作者
Yongping Wang,Saleem Riaz,Zhilong Bao,Wenqian Zhang
摘要
For a class of linear discrete time invariant stochastic switched systems with repetitive operation characteristics, a novel P-type accelerated iterative learning control algorithm with association correction is proposed. Firstly, the concrete form of accelerated learning law is given, and the generation of correction in the algorithm is explained in detail; secondly, on the premise that the switching sequence does not change along the iteration axis but only along the time axis, the convergence of the algorithm is strictly mathematically proved by using the super vector method, and the sufficient conditions for the convergence of the algorithm are given; finally, the theoretical results show that the convergence speed mainly depends on the controlled object, the proportional gain of the control law, the switching sequence, the correction coefficient, the association factor, and the size of the learning interval. Simulation results show that the proposed algorithm has a faster convergence speed than the traditional open-loop P-type algorithm under the same conditions.
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