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

• Published in: International journal of control, automation, and systems Vol. 15; no. 6; pp. 2921 - 2929
• Main Authors: Yao, Lina, Zhang, Yanna
• Format: Journal Article
• Language: English
• Published: Bucheon / Seoul Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers 01-12-2017; Springer Nature B.V; 제어·로봇·시스템학회
• Subjects: Active control; Adaptive systems; Basis functions; Computer simulation; Control; Control algorithms; Control systems; Control theory; Dynamical systems; Engineering; Fault diagnosis; Fault tolerance; Fuzzy systems; Intelligent Control and Applications; Mathematical models; Mechatronics; Neural networks; Nonlinear dynamics; Normal distribution; Predictive control; Probability density functions; Radial basis function; Robotics; Stability analysis; Stochastic control theory; 제어계측공학; Fault diagnosis; model predictive control; radial basis function; T-S fuzzy model; probability density function
• ISSN: 1598-6446; 2005-4092
• DOI: 10.1007/s12555-016-0370-6

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.