Fuzzy modeling-based fault diagnosis and fault tolerant control for the non-Gaussian nonlinear singular stochastic distribution system

Abstract

Ahstract- In this paper, a new fault diagnosis and fault tolerant control (FTC) algorithm is presented for the non-Gaussian nonlinear singular stochastic distribution control (SDC) system based on fuzzy modeling. Linear fuzzy logic models are used to approximate the output probability density function (PDF) and Takagi-Sugeno fuzzy models are employed to describe the nonlinear relations between fuzzy weight dynamics and the control input. Fault diagnosis is based on the use of a fuzzy fault diagnosis observer, with which the fault can be diagnosed. Based on the estimated fault and the desired PDF information, a fuzzy fault tolerant controller is designed to make the postfault PDF still track the given distribution. At last, simulation results on a flame shape distribution control system is given to demonstrate the effectiveness of the proposed algorithm, and satisfactory results have been obtained.