Adaptive Fault Estimation and Accommodation for Distributed Parameter Systems with Coupled Parabolic Partial Differential Equations
Abstract
This paper presents a novel model-based approach for fault detection, estimation and accommodation in linear distributed parameter systems (DPS) governed by parabolic partial differential equations (PDEs), prone to sensor and actuator faults. Unlike traditional methods, our approach utilises a filter-based observer directly employing the PDE representation and relies solely on boundary measurements, eliminating the need for extensive sensor arrays. By generating fault detection residuals from a comparison of measured and observer outputs, our method offers efficient fault detection. Innovative parameter update laws facilitate accurate estimation of fault parameters, enabling precise adjustment of the nominal controller to mitigate fault effects. Rigorous Lyapunov analysis ensures the robustness of our approach. Additionally, we derive a formula for estimating time-to-accommodation (TTA), providing valuable insights into fault resolution timelines. Simulations on a linearised diffusion process validate the effectiveness of our scheme, highlighting its superiority over existing approaches.
Recommended Citation
F. Ferdowsi et al., "Adaptive Fault Estimation and Accommodation for Distributed Parameter Systems with Coupled Parabolic Partial Differential Equations," Journal of Control and Decision, Taylor and Francis Group; Taylor and Francis, Jan 2024.
The definitive version is available at https://doi.org/10.1080/23307706.2024.2388560
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
Keywords and Phrases
adaptive control; adaptive estimation; distributed parameter systems; Fault detection; fault-tolerant control; partial differential equations
International Standard Serial Number (ISSN)
2330-7714; 2330-7706
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Taylor and Francis Group; Taylor and Francis, All rights reserved.
Publication Date
01 Jan 2024
