Fault isolation and fault-tolerant control for Takagi-Sugeno fuzzy time-varying delay stochastic distribution systems

Kang, Yunfeng; Yao, Lina; Wang, Hong

doi:10.1109/tfuzz.2021.3053320

Fault isolation and fault-tolerant control for Takagi-Sugeno fuzzy time-varying delay stochastic distribution systems

Journal Article · Wed Jan 20 23:00:00 EST 2021 · IEEE Transactions on Fuzzy Systems

DOI:https://doi.org/10.1109/tfuzz.2021.3053320· OSTI ID:1813233

Kang, Yunfeng ^[1]; Yao, Lina ^[1]; Wang, Hong ^[2]

Zhengzhou University (China)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

A fault isolation, estimation, and fault-tolerant control (FTC) scheme for nonlinear time-varying delay stochastic distribution control systems was presented in this paper. The Takagi-Sugeno fuzzy model was adopted to approach the nonlinear dynamics of time-varying delay systems. According to the output equivalence principle and Laplace transformation, an augmented state vector was given to solve the time-varying delay problem. When multiple actuator faults and interference occur simultaneously, fault detection, isolation and fault estimation was designed to obtained the fault information. To decouple faults and obtain the value and location information of the fault, the system was separated into two parts through the designed multiple conversion matrices, in which one subsystem was only affected by one actuator fault. This has simplified the design of fault isolation and estimation. A adaptive observer for fault estimation was given. Then, fault information such as the time, location, and size was determined. The observer gain matrices were calculated using linear matrix inequality (LMI). When a fault was detected and diagnosed, a FTC algorithm was devised using the proportional-integral control scheme to compensate the fault as much as possible. It has been shown that even if multiple faults actuator occurred simultaneously, the FTC controller still ensured the output probability density function of the system traced the desired probability density function when a fault occurred. Finally, the expected results were obtained through the simulation example, which confirmed the effectiveness of the method.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1813233

Journal Information:: IEEE Transactions on Fuzzy Systems, Journal Name: IEEE Transactions on Fuzzy Systems Journal Issue: 4 Vol. 30; ISSN 1063-6706

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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