NDLI: Fault detection for a class of strict-feedback systems via deterministic learning

Content Provider	IEEE Xplore Digital Library
Author	Junmin Hu Cong Wang
Copyright Year	2012
Description	Author affiliation: College of Automation Science and Engineering and Center for Control and Optimization, South China University of Technology, Guangzhou, 510641 (Junmin Hu; Cong Wang)
Abstract	A fault detection approach is proposed for a class of strict-feedback nonlinear systems with dynamic uncertainties in this paper. We present the approach based on deterministic learning consists of three phases: Firstly, by combining adaptive neural with backstepping method, a neural networks (NN) controller is designed for controlling a class of nonlinear closed-loop systems with dynamic uncertainties in strict-feedback form so as to achieve guaranteed the convergence of tracking errors in a finite time. Secondly, in the learning phase, the overall closed-loop system dynamics underlying normal and fault modes are locally accurately approximated through deterministic learning. The obtained knowledge of system dynamics is stored in constant RBF networks. Finally, in the detecting phase, a bank of estimators are constructed using the constant RBF networks to represent the learning normal and fault modes. By comparing the set of estimators with the monitored system, a set of residuals are generated, and the average L1 norms of the residuals are taken as the measure of the differences between the dynamics of the monitored system and the dynamics of the learning normal and fault modes. The occurrence of a fault can be rapidly detected according to the smallest residual principle. Simulation studies are given to demonstrate the effectiveness of the proposed approach.
Starting Page	82
Ending Page	87
File Size	1920786
Page Count	6
File Format	PDF
ISBN	9781457721441
e-ISBN	9781457721434
DOI	10.1109/ICICIP.2012.6391499
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault detection Artificial neural networks Radial basis function networks Trajectory Approximation methods Monitoring Convergence
Content Type	Text
Resource Type	Article

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