Iterative learning based fault detection and estimation in nonlinear systems

作     者：Wei Cao Wang Cong Ming Sun 

作者机构：College of Automation Harbin Engineering University Harbin 150001 E R. China College of Computer and Control Engineering Qiqihar University Qiqihar 161006 E R. China 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2012年第23卷第3期

页      面：419-424页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 081104[工学-模式识别与智能系统] 08[工学] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 0838[工学-公安技术] 0811[工学-控制科学与工程] 071102[理学-系统分析与集成] 081103[工学-系统工程] 

基　　金：supported by the National Natural Science Foundation of China (61100103) 

主　　题：iterative learning nonlinear system fault detection fault estimation. 

摘      要：Aiming at a class of nonlinear systems that contains faults, a novel iterative learning scheme is applied to fault detec- tion, and a novel algorithm of fault detection and estimation is proposed. This algorithm first constructs residual signals by the output of the practical system and the output of the designed fault tracking estimator, and then uses the residuals and the difference- value signal of the adjacent two residuals to gradually revise the introduced virtual faults, which can cause the virtual faults to close to the practical faults in systems, thereby achieving the goal of fault detection for systems. This algorithm not only makes full use of the existing valid information of systems and has a faster tracking con- vergent speed than the proportional-type （P-type） algorithm, but also calculates more simply than the proportional-derivative-type （PD-type） algorithm and avoids the unstable effects of differential operations in the system. The final simulation results prove the validity of the proposed algorithm.