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Fault diagnosis and model predictive tolerant control for non-Gaussian stochastic distribution control systems based on T-S fuzzy model

  • Lina YaoEmail author
  • Yanna Zhang
Intelligent Control and Applications

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.

Keywords

Fault diagnosis model predictive control probability density function radial basis function T-S fuzzy model 

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Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.School of Electrical EngineeringZhengzhou UniversityZhengzhouChina

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