Model reference adaptive tracking control for hydraulic servo systems with nonlinear neural-networks

被引：42

作者：

Yao, Zhikai ^{[1
]}

Yao, Jianyong ^{[1
]}

Yao, Feiyu ^{[1
]}

Xu, Qiang ^{[1
]}

Xu, Minrui ^{[1
]}

Deng, Wenxiang ^{[1
]}

机构：

[1] Nanjing Univ Sci & Technol, Sch Mech Engn, Nanjing 210094, Peoples R China

来源：

ISA TRANSACTIONS | 2020年 / 100卷

基金：

中国国家自然科学基金;

关键词：

Model reference; Hydraulic systems; Robust and adaptive control; Neural networks; Asymptotic stability; ROBUST-CONTROL; MOTION CONTROL; ASYMPTOTIC TRACKING; ACTUATORS; FEEDFORWARD;

D O I：

10.1016/j.isatra.2019.11.027

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

It is well known that hydraulic systems typically suffer from heavy disturbances including parametric uncertainties and unknown disturbances. In order to attain high performance tracking control, this paper proposes a composite design of nonlinear neural-networks (NN) and continuous robust integral of the sign of the error (RISE) feedback controller. The control development incorporates a NN feedforward component to have a compensation for unknown state-dependent disturbances and to further improve the accuracy of feedforward compensation, meanwhile input parameter is updated online. To achieve asymptotic stability, a novel RISE term with NN-based feedforward component is developed for the first time to enable the incorporation of model reference adaptive control structure where acceleration signal is not employed. The proposed controller guarantees controlled hydraulic system a semi-global asymptotic stability. For the experimental results, the prescribed transient performance is tested under rectangular trajectory and the steady state performance is tested under sinusoidal trajectory. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.

引用

页码：396 / 404

页数：9

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