Neural networks in design of iterative learning control for nonlinear systems

被引：17

作者：

Patan, Krzysztof ^{[1
]}

Patan, Maciej ^{[1
]}

Kowalow, Damian ^{[1
]}

机构：

[1] Univ Zielona Gora, Inst Control & Computat Engn, Ul Szafrana 2, PL-65516 Zielona Gora, Poland

来源：

IFAC PAPERSONLINE | 2017年 / 50卷 / 01期

关键词：

Iterative learning control; neural networks; control design; machine learning; nonlinear systems; MACHINE;

D O I：

10.1016/j.ifacol.2017.08.2277

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

An approach to control design for nonlinear system working under repetitive regime is presented. The general iterative learning control scheme is enhanced with a neural network controller to reduce the uncertainty of the model used for the control design. To achieve this goal an effective data-driven technique for training neural controller is developed. In result, in each process trial, both the control performance and process model can be substantially improved. Also, the stability issues of the neural controller are discussed. Finally, as an illustration of the proposed approach the application to nonlinear pneumatic servomechanism is given. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

引用

页码：13402 / 13407

页数：6

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