Fault isolation and estimation for non-Gaussian stochastic distribution control systems based on T-S fuzzy model

Abstract

A Takagi-Sugeno (T-S) fuzzy model is applied to approximate the nonlinear dynamics of stochastic distribution control (SDC) systems, in which linear radial basis function (RBF) net work is adopted to approximate the output probability density function (PDF) for non-Gaussian SDC systems. Considering the situation that disturbance and multiple actuator faults may occur at the same time, fault detection, isolation and estimation is conducted. The decoupling is realized by using nonsingular linear coordinate transformation. In order to determine the fault location and structure, multiple transformation matrices are designed to divide the system into two subsystems. One of the subsystems contains only one actuator fault, which facilitates the fault isolation and estimation later. In this paper, the earliest time fault occurred and fault location is given. The adaptive observers for fault estimation are given respectively. By using the Lyapunov stability theorem the observation error dynamic system is proved stable. The observer parameters are determined by LMI. Finally, computer simulation results show the effectiveness of the proposed algorithm.