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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 705725, 9 pages
Research Article

Robust Fault Detection for a Class of Uncertain Nonlinear Systems Based on Multiobjective Optimization

Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education, Department of Automation, East China University of Science and Technology, Shanghai 200237, China

Received 30 August 2014; Accepted 12 October 2014

Academic Editor: Wei Zhang

Copyright © 2015 Bingyong Yan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A robust fault detection scheme for a class of nonlinear systems with uncertainty is proposed. The proposed approach utilizes robust control theory and parameter optimization algorithm to design the gain matrix of fault tracking approximator (FTA) for fault detection. The gain matrix of FTA is designed to minimize the effects of system uncertainty on residual signals while maximizing the effects of system faults on residual signals. The design of the gain matrix of FTA takes into account the robustness of residual signals to system uncertainty and sensitivity of residual signals to system faults simultaneously, which leads to a multiobjective optimization problem. Then, the detectability of system faults is rigorously analyzed by investigating the threshold of residual signals. Finally, simulation results are provided to show the validity and applicability of the proposed approach.