具有迭代初始误差的高相对度线性离散系统的迭代学习控制

本文针对具有迭代初始误差的高相对度线性多变量离散系统,提出了一种P型的迭代学习控制算法.通过将迭代学习控制系统的二维运动过程描述为一维的线性离散系统,证明了该迭代学习控制算法的收敛性及其收敛的充要条件.该迭代学习控制算法通过对系统前次重复运动过程中的输入和跟踪误差信号进行学习,来不断地调整输入量,使得系统在经过一定次数的学习以后,在初始时间点以外的实际输出趋于期望输出.数值仿真结果表明了所提出算法的有效性.

Iterative learning control for linear discrete systems with high relative degree and iterative initial error

This paper presents a P-type iterative learning control algorithm for linear multi-variable discrete systems with high relative degree and iterative initial errors. The convergence of the proposed iterative learning control algorithm with necessary and sufficient condition is proved by converting the two-dimensional iterative learning control process into a one-dimensional linear discrete system. The proposed iterative learning control method updates the control input iteratively by a learning mechanism using the information of errors and inputs in the preceding trials such that the tracking performance of the system outputs beyond the initial time point is improved. A numerical example is used to illustrate the effectiveness of the proposed iterative learning control technique.