Title: Robust H_∞; fault diagnosis for stochastic distribution systems with disturbance rejection performance

Authors: Lina Yao; Ziyang Yin; Hong Wang

Addresses: School of Electrical Engineering, Zhengzhou University, Henan Zhengzhou, 450001, Henan, China ' School of Electrical Engineering, Zhengzhou University, Henan Zhengzhou, 450001, Henan, China ' Control Systems Centre, University of Manchester, Manchester M13 9PL, UK

Abstract: The task of robust fault diagnosis of stochastic distribution control (SDC) systems with unknown external disturbance is to use the measured input and the system output probability density function (PDF) to still obtain possible fault information of the system. In this paper, an enhanced robust fault diagnosis scheme is presented for the non-Gaussian stochastic distribution system. A B-spline neural network model is established, where a static neural network is applied to model the output PDFs and a non-linear dynamic model is used to describe the relationships between the input and the weight. The composite observer for SDCs is constructed by combining a fault diagnosis observer with a disturbance observer, with which the fault can be diagnosed and the disturbance can be rejected simultaneously. Lastly, an illustrated example is given to demonstrate the effectiveness of the proposed algorithm, and satisfactory results have been obtained.

Keywords: fault diagnosis; probability density function; PDF; stochastic distribution control; linear matrix inequalities; LMI; robust H-infinity; disturbance rejection; B-spline neural networks; nonlinear modelling; dynamic modelling.

DOI: 10.1504/IJMIC.2014.060732

International Journal of Modelling, Identification and Control, 2014 Vol.21 No.3, pp.288 - 294

Published online: 07 Jun 2014 *

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