Robust fault detection and adaptive parameter identification for DC‐DC converters via switched systems. (6th September 2020)

• Record Type: Journal Article
• Authors: Li, Jian; Pan, Kunpeng; Su, Qingyu
• Is Part Of: International journal of adaptive control and signal processing. Volume 34:Number 11(2020)
• Page Start: 1642
• Page End: 1657
• Publication Date: 2020-09-06
• Subjects: adaptive parameter identification -- DC‐DC converters -- fault detection -- fault identification -- switched systems; Adaptive control systems -- Periodicals; Adaptive signal processing -- Periodicals; 629.836
• DOI: 10.1002/acs.3170
• Languages: English
• ISSNs: 0890-6327

Abstract

Summary: In this paper, the problem of fault detection and identification for DC‐DC converters is presented. First, switched systems model and fault model are analyzed based the switched characteristics of the DC‐DC converters, taking the DC‐DC buck converter as an example. According to the switched Lyapunov function technique, a fault detection observer and a bank of linear switched fault identification observers are designed for the switched systems. Next, the fault detection observer detects the fault based on the residual produced by the observer output and actual output. After the fault is detected, fault identification observers are activated. The location of fault is identified by comparing the residual evaluation functions. Meanwhile, the adaptive parameter identification is achieved by choosing an appropriate adaptive law. Finally, in order to show the feasibility of the fault detection and identification, the simulation results are given in this article.