Title: Fault diagnosis and fault tolerant control for the non-Gaussian nonlinear stochastic distribution control system using Takagi-Sugeno fuzzy model
Authors: Lina Yao; Haoran Wang
Addresses: School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China ' School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
Abstract: For the non-Gaussian nonlinear stochastic distribution control system using Takagi-Sugeno fuzzy model, the nonlinear dynamic system is converted to a linear system. A fault diagnosis algorithm using RBF neural network and a sliding mode fault tolerant control algorithm is presented. A new adaptive fault diagnosis algorithm is adopted to diagnose the gradual fault that occurred in the system, and the stability of the observation error system is proved. Differential evolution (DE) algorithm is used to optimise the central vector and width vector of RBF neural network. The sliding mode control algorithm is used to reconfigure the controller, based on the fault estimation information. The post-fault probability density function (PDF) can still track the given distribution. Finally, simulation results show the effectiveness of the proposed fault diagnosis and fault tolerant control algorithm.
Keywords: fault diagnosis; RBF neural network; differential evolution; DE; fault tolerant control; sliding mode control.
International Journal of Modelling, Identification and Control, 2018 Vol.29 No.1, pp.22 - 30
Available online: 19 Jan 2018 *Full-text access for editors Access for subscribers Free access Comment on this article