Robust higher-order ILC for non-linear discrete-time systems with varying trail lengths and random initial state shifts
Robust higher-order ILC for non-linear discrete-time systems with varying trail lengths and random initial state shifts
复制标题
适用于具有变化轨迹长度和随机初始状态转移的非线性离散时间系统的鲁棒高阶 ILC
DOI：
10.1049/iet-cta.2017.0008
复制
发表时间：
2017
影响因子：
2.6
通讯作者：
Xiao-Dong Li
中科院分区：
文献类型：
--
作者：
Yun-Shan Wei;Xiao-Dong Li
针对非线性离散时间系统，提出了一种鲁棒迭代学习控制方法，该方法中，跟踪长度和初始状态偏移都可以在迭代域内随机变化。所提出的高阶迭代学习控制律保证了当迭代次数趋于无穷大时，迭代学习控制在期望输出跟踪周期处的跟踪误差满足数学期望有界，并且跟踪误差的界与随机初始状态偏移成正比.具体地说，当初始状态转移的期望为零时，数学期望中的ILC跟踪误差可以被驱动为零。两个数值例子证明了所提出的高阶迭代学习控制律的有效性。
This study addresses a robust iterative learning control (ILC) scheme for non-linear discrete-time systems in which both the trail lengths and the initial state shifts could be randomly variant in iteration domain. The proposed higher-order ILC law guarantees that as the iteration number goes to infinity, the ILC tracking errors at the desired output trail period are bounded in mathematical expectation, and the bound of tracking errors is proportional to the random initial state shifts. Specifically, the ILC tracking errors in mathematical expectation can be driven to zero as the expectation of initial state shifts is zero. Two numerical examples are carried out to demonstrate the effectiveness of the proposed higher-order ILC law.