Varying trail lengths-based iterative learning control for linear discrete-time systems with vector relative degree
Varying trail lengths-based iterative learning control for linear discrete-time systems with vector relative degree
复制标题
具有向量相对度的线性离散时间系统基于变轨迹长度的迭代学习控制
DOI：
10.1080/00207721.2017.1309590
复制
发表时间：
2017
影响因子：
4.3
通讯作者：
Xiao-Dong Li
中科院分区：
文献类型：
--
作者：
Yun-Shan Wei;Xiao-Dong Li
摘要针对具有向量相对度的线性离散时间多输入多输出（MIMO）系统，针对迭代变尾长和初始状态随机漂移的问题，首次提出了一种基于平均算子的PD型迭代学习控制律。所提出的PD型迭代学习控制律包含了对初始状态偏移的初始修正作用，并在初始时刻之后跟踪参考轨迹。作为PD型迭代学习控制律的特例，引入了P型和D型迭代学习控制律。证明了对于具有向量相对度的线性离散时间MIMO系统，所提出的三种迭代学习控制律能够使基于变迹长的迭代学习控制跟踪误差在初始时刻之后达到数学期望为零。数值例子说明了所提出的迭代学习控制律的有效性。
ABSTRACT In this article, to tackle with the iteration-varying trail lengths and random initial state shifts, an average operator-based PD-type iterative learning control (ILC) law is firstly presented for linear discrete-time multiple-input multiple-output (MIMO) systems with vector relative degree. The proposed PD-type ILC law includes an initial rectifying action against initial state shifts, and pursues the reference trajectory tracking beyond the initial time points. As special cases of the PD-type ILC law, P-type and D-type ILC laws are then introduced. It is proved that for linear discrete-time MIMO systems with vector relative degree, the three proposed ILC laws can drive the varying trail lengths-based ILC tracking errors to zero in mathematical expectation beyond the initial time points. A numerical example is used to illustrate the effectiveness of the proposed ILC laws.