Title: Fault detection, diagnosis and tolerant control for non-Gaussian stochastic distribution systems using a rational square-root approximation model
Authors: Li-Na Yao, Aiping Wang, Hong Wang
Addresses: School of Electrical Engineering, Zhengzhou University, Zhengzhou, Henan 450001, PR China. ' Institute of Computer Sciences, Anhui University, Hefei, Anhui, PR China. ' Control System Centre, The University of Manchester, P.O. Box 88, Manchester M60 QD, UK
Abstract: Stochastic Distribution Control (SDC) systems are a group of systems where the outputs considered are the measured Probability Density Functions (PDFs) of the system output whilst subjected to a normal crisp input. The purpose of the control algorithm design of such systems is to choose a control input such that the PDF of the system output can follow a prespecified PDF as close as possible. Using the rational square-root B-spline model to represent the dynamics between the output PDF and the input, in this paper a non-linear adaptive observer-based fault diagnosis algorithm is proposed to diagnose the fault in the dynamic part of such systems. Using the estimation to the unknown fault, a fault tolerant control via a controller reconfiguration is proposed, where it has been shown that a good output PDF tracking can still be realised when fault occurs in the system. A simulated example is given to illustrate the use of the proposed algorithm.
Keywords: rational square-root approximation; non-Gaussian stochastic systems; fault diagnosis; fault tolerant control; fault detection; stochastic distribution control; controller reconfiguration.
International Journal of Modelling, Identification and Control, 2008 Vol.3 No.2, pp.162 - 172
Available online: 08 Jul 2008 *Full-text access for editors Access for subscribers Purchase this article Comment on this article