Nonrepetitive Fault Estimation for Continuous-Time Switched Systems via Iterative Learning Observer With Current Feedback

This brief studies the iterative learning-based fault estimation issue for continuous-time switched linear systems with actuator faults. Unlike the existing results, a kind of iteration-varying faults is considered. A new current feedback-based iterative learning fault estimator is developed to cope with such iteration-varying faults. The consideration of current information can improve fault estimation accuracy compared to traditional iterative learning estimation law. Under the mode-dependent average dwell time (MDADT) switching, the robust monotonic convergence of iterative learning estimator is guaranteed. Finally, the Chua’s circuit system is employed to verify the effectiveness and superiority of the proposed iterative learning estimation algorithm with current feedback.