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) neural network is adopted to approximate the output probability density function (PDF) of non-Gaussian SDC systems. Considering the situation that fault may occur, a fuzzy adaptive fault diagnosis observer is designed to estimate the fault value. Besides, the Lyapunov stability theory is used to analyse the stability of the observation error system. Based on the fault estimation information and model predictive control (MPC) algorithm, the active fault tolerant control strategy is given. Finally, a simulation example is given to verify the effectiveness of the proposed control algorithm.
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