Data-driven predictive point-to-point iterative learning control

被引：3

作者：

Zhang, Xueming ^{[1
]}

Hou, Zhongsheng ^{[1
,2
,3
,4
,5
]}

机构：

[1] Qingdao Univ, Dept Automation, Qingdao 266071, Peoples R China

[2] Harbin Inst Technol, Harbin, Peoples R China

[3] Yale Univ, New Haven, CT USA

[4] Beijing Jiaotong Univ, Beijing, Peoples R China

[5] Qingdao Univ, Sch Automation, Qingdao, Peoples R China

来源：

NEUROCOMPUTING | 2023年 / 518卷

基金：

美国国家科学基金会;

关键词：

Predictive control; Point-to-point iterative learning control; (PTPILC); Data-driven control (DDC); Repetitive non-affine nonlinear systems; DISCRETE-TIME-SYSTEMS; ADAPTIVE ILC;

D O I：

10.1016/j.neucom.2022.11.014

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Integrating the idea of predictive control and point-to-point iterative learning control, this paper presents a data-driven predictive point-to-point iterative learning control scheme for a class of unknown repeti-tive non-affine nonlinear SISO systems. The tracking task is driven by the optimal control input sequence generated by the proposed algorithm, and the tracking errors at the specified sampling time instants are minimized. The advantages of this scheme are that the structure of the controller and its stability analysis both are based on an equivalent dynamic linearization data model of the nonlinear system, and the pro-posed scheme does not involve the operation of matrix inversion. Numerical simulations verify the effec-tiveness of this method.(c) 2022 Elsevier B.V. All rights reserved.

引用

页码：431 / 439

页数：9

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