Title: Fault diagnosis and minimum entropy fault tolerant control for non-Gaussian singular stochastic distribution systems using square-root approximation

Authors: Lina Yao; Yacun Guan; Aiping Wang

Addresses: School of Electrical Engineering, Zhengzhou University, Henan Zhengzhou, 450001, Henan, China ' School of Electrical Engineering, Zhengzhou University, Henan Zhengzhou, 450001, Henan, China ' School of Computer Science and Technology, Anhui University, Hefei 230039, Anhui, China

Abstract: The fault diagnosis (FD) and minimum entropy fault tolerant control (FTC) algorithms are proposed for non-Gaussian singular stochastic distribution control (SDC) systems in this paper. Different from general SDC systems, in singular SDC systems, the relationship between the weight vector and the control input is expressed by a singular state space model, which increases the difficulty in the design of fault diagnosis and fault tolerant control. A non-singular state transformation is made to transform the singular dynamic system into a differential-algebraic system. An iterative learning observer (ILO) is designed for fault estimation. The concept of entropy is introduced to fault tolerant control of the non-Gaussian stochastic distribution system when the target probability density function (PDF) is not known in advance. Based on the estimated fault information, the controller is reconfigured by minimising the performance function with regard to the entropy subjected to mean constraint. The reconfigured controller can make the output of the post-fault SDC system still have minimum uncertainty, leading to 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.

Keywords: stochastic distribution control; non-Gaussian SDC; singular SDC; iterative learning observer; ILO; mean constraint; fault diagnosis; minimum entropy; fault tolerant control; square-root approximation; fault tolerance; simulation.

DOI: 10.1504/IJMIC.2015.072620

International Journal of Modelling, Identification and Control, 2015 Vol.24 No.3, pp.206 - 215

Received: 23 Jan 2015
Accepted: 19 Mar 2015
Published online: 22 Oct 2015 *

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