Self-Correcting Iterative Learning-Based Fault Estimation for Parabolic Distributed Parameter Systems

• Published in: IEEE transactions on circuits and systems. II, Express briefs Vol. 71; no. 1; p. 1
• Main Authors: Xu, Shuiqing, Wang, Lejing, Feng, Li, Yang, Xi, Chai, Yi, Du, Haibo, Zheng, Wei Xing
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Circuit faults; Convergence; Distributed parameter systems; Estimation; Faults; Iterative methods; Learning; Mathematical analysis; Observers; Parabolic distributed parameter systems; self-correcting iterative learning; spatio-temporal faults; Time-domain analysis; time-domain faults
• ISSN: 1549-7747; 1558-3791
• DOI: 10.1109/TCSII.2023.3299626

This article presents a novel method for simultaneously estimating time-domain faults and spatio-temporal faults in parabolic distributed parameter systems (PDPSs). Initially, an iterative learning observer that considers both temporal and spatial variations is developed to estimate faults in PDPS. Subsequently, a novel self-correcting iterative learning (SCIL)-based fault estimation law is designed to enhance the speed and accuracy of fault estimation. Meanwhile, by employing the λ-norm method, L2-norm method, and mathematical induction method, it becomes feasible to derive the convergence conditions and obtain the gain matrices in a straightforward manner. Finally, simulation results are provided to verify the applicability of the developed method, demonstrating its capability to estimate complex fault modes and its superior performance in fault estimation.