Minimum entropy fault tolerant control for non-Gaussian singular stochastic distribution system

Abstract

This paper presents a new fault diagnosis (FD) and fault tolerant control (FTC) algorithm for a discrete-time non- Gaussian singular stochastic distribution control (SDC) system. Different from general SDC systems, the difficulty increases in the FD and FTC design of the singular SDC system as the relationship between the weights and the control input is expressed by a singular state space mode. Adaptive observer is used to diagnose the fault of the singular stochastic distribution system. Furthermore, the gain of the observer and adaptive updated law can be formulated by solving the corresponding linear matrix inequality (LMI) approach. Entropy is used to represent the output randomness of the non-Gaussian stochastic system when the target probability density function (PDF) is not known in advance. Based on the estimated fault information, the controller is reconfigured by minimizing the performance function with regard to the entropy subject to mean constraint. The reconfigured controller can make the output of the post-fault SDC system still have minimum uncertainty, leading to the minimum entropy FTC of the non-Gaussian singular SDC system. Computer simulations are given to demonstrate the validity of the fault diagnosis and minimum entropy FTC algorithms.