Model free adaptive control with disturbance rejection based on modified Kalman filter

被引：3

作者：

Lu X.-Y. ^{[1
]}

Hou Z.-S. ^{[1
]}

机构：

[1] School of Automation, Qingdao University, Shandong, Qingdao

来源：

Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2022年 / 39卷 / 07期

基金：

中国国家自然科学基金;

关键词：

disturbance rejection; Kalman filter; minimum variance estimation; model free adaptive control;

D O I：

10.7641/CTA.2022.10355

中图分类号：

学科分类号：

摘要：

For the problem that the model free adaptive control method has poor control performance under the influence of measurement disturbance. In this paper, a new model free adaptive control scheme with disturbance rejection is proposed. Firstly, based on the dynamic linearized data model of the controlled system and the statistical characteristics of measurement disturbance, an improved Kalman filter based on the system input and output data is derived under the minimum variance estimation criterion. Then a new model free adaptive control scheme with disturbance rejection is proposed. This scheme can realize the model free adaptive control of the system under the strong measurement disturbance only by using the input and output data of the controlled system. The simulation results show that, compared with the existing disturbance rejection model free adaptive control scheme, the new scheme can effectively suppress the measurement disturbance when tracking the constant reference signal and the time-varying reference signal, and can obtain the smaller tracking error and the greater data signal-to-noise ratio while it is more applicable. © 2022 South China University of Technology. All rights reserved.

引用

页码：1211 / 1218

页数：7

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