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

Shuiqing Xu

doi:10.1109/TCSII.2023.3299626

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

2024-01-01

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DOI：10.1109/TCSII.2023.3299626

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This brief 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 lambda-norm method, L-2-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.

Keywords:

Parabolic distributed parameter systems

time-domain faults

spatio-temporal faults

self-correcting iterative learning

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

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