Fault detection and estimation based on adaptive iterative learning algorithm for nonlinear systems

被引：0
作者：
Chen Z.-Q. [1 ]
Han L. [2 ]
Hou Y.-D. [1 ,3 ]
机构：
[1] School of Computer and Information Engineering, Henan University, Kaifeng, 475004, Henan
[2] Miami College of Henan University, Kaifeng, 475004, Henan
[3] Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, 475004, Henan
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2020年 / 37卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Adaptive; Fault detection; Fault estimation; Iterative learning; Runge-Kutta;
D O I：
10.7641/CTA.2019.90097
中图分类号：
学科分类号：
摘要：
Aiming at the problem that the iterative learning algorithm has a large estimation error and slow convergence speed in the process of nonlinear system fault detection and estimation. An adaptive iterative learning algorithm based on Runge-Kutta fault estimation observer model is proposed, which can effectively reduce the error of fault estimation; and the H∞ performance index is introduced to improve the convergence rate of the fault estimation observer. The algorithm first designs the fault detection observer to detect the fault, then designs the fault estimation observer, and the adaptive algorithm is combined with the iterative learning strategy, so that the estimated fault gradually approaches the real fault, thus achieving accurate detection and estimation of many common faults in the nonlinear system. Finally, the effectiveness of the proposed algorithm is verified by the actuator fault simulation of the mechanically driven motor. © 2020, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.
引用
收藏
页码：837 / 846
页数：9
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