Noise-Tolerant ZNN-Based Data-Driven Iterative Learning Control for Discrete Nonaffine Nonlinear MIMO Repetitive Systems  

作　　者：Yunfeng Hu Chong Zhang Bo Wang Jing Zhao Xun Gong Jinwu Gao Hong Chen 

机构地区：[1]State Key Laboratory of Automotive Simulation and Control,Jilin University,Jilin University,Changchun 130025,China [2]College of Communication Engineering,Jilin University,Jilin University,Changchun 130025,China [3]Department of Electromechanical Engineering,University of Macao,Macao 999078,China [4]School of Artificial Intelligence,Jilin University,Changchun 130012,China [5]IEEE [6]College of Electronics and Information Engineering,Tongji University,Shanghai 200092,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2024年第2期344-361,共18页自动化学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(U21A20166);in part by the Science and Technology Development Foundation of Jilin Province (20230508095RC);in part by the Development and Reform Commission Foundation of Jilin Province (2023C034-3);in part by the Exploration Foundation of State Key Laboratory of Automotive Simulation and Control。

摘　　要：Aiming at the tracking problem of a class of discrete nonaffine nonlinear multi-input multi-output(MIMO) repetitive systems subjected to separable and nonseparable disturbances, a novel data-driven iterative learning control(ILC) scheme based on the zeroing neural networks(ZNNs) is proposed. First, the equivalent dynamic linearization data model is obtained by means of dynamic linearization technology, which exists theoretically in the iteration domain. Then, the iterative extended state observer(IESO) is developed to estimate the disturbance and the coupling between systems, and the decoupled dynamic linearization model is obtained for the purpose of controller synthesis. To solve the zero-seeking tracking problem with inherent tolerance of noise,an ILC based on noise-tolerant modified ZNN is proposed. The strict assumptions imposed on the initialization conditions of each iteration in the existing ILC methods can be absolutely removed with our method. In addition, theoretical analysis indicates that the modified ZNN can converge to the exact solution of the zero-seeking tracking problem. Finally, a generalized example and an application-oriented example are presented to verify the effectiveness and superiority of the proposed process.

关 键 词：Adaptive control control system synthesis data-driven iterative learning control neurocontroller nonlinear discrete time systems 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程]