Multiple Faults Identification for Nonlinear Control Systems based on T-S Fuzzy Models

Abstract

AbstractThe identification of multiple faults in a control system where some sensors and/or actuators may be faulty simultaneously is of great importance for enhancing the control system reliability. In this paper, a novel method for multiple faults identification of nonlinear systems based on T-S fuzzy model and parameter estimation is presented. A series of fully decoupled parity equations sensitive to special groups, which are called the susceptible groups, are designed for the local linear models. Then the residuals are generated by fusing the residuals generated from the parity equations of local linear models using the T-S model. A parameter estimator based on recursive least square is designed to identify the multiple faults from the information contained in the residuals. A simulation example on an induction motor of a railway traction system is given for illustration. Results show that the new approach can be used to identify multiple faults in nonlinear systems.