Fuzzy Fault Detection Filter Design for Nonlinear Distributed Parameter Systems
Linlin Li,
Steven X. Ding,
Kaixiang Peng,
Jianbin Qiu,
Ying Yang
Affiliations
Linlin Li
Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China
Steven X. Ding
Institute for Automatic Control and Complex Systems (AKS), University of Duisburg-Essen, Duisburg, Germany
Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China
Jianbin Qiu
Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, Harbin, China
Department of Mechanics and Engineering Science, State Key Laboratory for Turbulence and Complex Systems, College of Engineering, Peking University, Beijing, China
This paper is devoted to investigating the observer-based fault detection (FD) filters for nonlinear distributed processes described by hyperbolic partial differential equations (PDEs). To this end, the PDE systems are first approximated by the Takagi-Sugeno fuzzy models with spatiotemporal uncertainties. Then, the fuzzy FD filter is developed for the hyperbolic PDE systems to guarantee that the residual signal is robust against process inputs including disturbances. The dynamic threshold is designed to ensure the real-time detection of potential faults. It is worth mentioning that the distributed weighting factors are used to weigh the residual signal such that the overall fault detectability can be optimized.
