Optimal iterative learning control under varying iteration lengths with input saturation

Mingchao You, Jie Shen, Liwei Li, Mouquan Shen
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Abstract

This article is concerned with optimized iterative learning control of linear time‐invariant systems against input saturation and varying iteration length. The varying length is described by a stochastic form. The corresponding iteration output is modified by the combination of the real iteration output and the desired one with the varying consideration. To optimize the tracking error, the constraint caused by input saturation is transformed to an unconstraint structure by a barrier method. Newton's method based optimal control law is adopted to minimize the quadratic index related to a modified tracking error. Rigorous theoretical derivations are presented to guarantee the convergence of tracking errors. An example is provided to confirm the validity of the proposed approach.
不同迭代长度下的最佳迭代学习控制与输入饱和度
本文涉及线性时变系统在输入饱和和迭代长度变化情况下的优化迭代学习控制。变化长度用随机形式描述。相应的迭代输出由实际迭代输出和所需迭代输出的组合进行修改,并考虑到变化因素。为了优化跟踪误差,输入饱和引起的约束通过障碍法转换为非约束结构。采用基于牛顿法的最优控制法则,以最小化与修正跟踪误差相关的二次指数。本文提出了严格的理论推导,以保证跟踪误差的收敛性。还提供了一个实例来证实所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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