Data-driven optimal terminal iterative learning control

被引：118
|
作者：
Chi, Ronghu [1 ]
Wang, Danwei [2 ]
Hou, Zhongsheng [3 ]
Jin, Shangtai [3 ]
机构：
[1] Qingdao Univ Sci & Technol, Sch Automat & Elect Engn, Qingdao 266042, Peoples R China
[2] Nanyang Technol Univ, Sch Elect & Elect Engn, Ctr E City, EXQUISITUS, Singapore 639798, Singapore
[3] Beijing Jiaotong Univ, Sch Elect & Informat Engn, Adv Control Syst Lab, Beijing 100044, Peoples R China
来源：
JOURNAL OF PROCESS CONTROL | 2012年 / 22卷 / 10期
基金：
美国国家科学基金会;
关键词：
Optimality-based design; Terminal ILC; Monotonic convergence; Linear and nonlinear discrete-time systems; Data-driven control; ALGORITHM;
D O I：
10.1016/j.jprocont.2012.08.001
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a data-driven optimal terminal iterative learning control (TILC) approach for linear and nonlinear discrete-time systems. The iterative learning control law is updated from only terminal output tracking error instead of entire output trajectory tracking error. The only required knowledge of a controlled system is that the Markov matrices of linear systems or the partial derivatives of nonlinear systems with respect to control inputs are bounded. Rigorous analysis and convergence proof are developed with sufficient conditions for the terminal ILC design and the results are developed for both linear and nonlinear discrete-time systems. Simulation results illustrate the applicability and effectiveness of the proposed approach. (C) 2012 Elsevier Ltd. All rights reserved.
引用
收藏
页码：2026 / 2037
页数：12
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