Switched Threshold-Based Fault Detection for Switched Nonlinear Systems With Its Application to Chua's Circuit System

In this paper, the fault detection problem for a class of switched nonlinear systems with unknown functions is solved. First, the unknown internal dynamics are approximated by the radial basis function neural networks due to their powerful approximation capabilities. Then, a switched nonlinear observer is developed. Based on the observer, a fault detection scheme is set up with a switched fault detection threshold instead of a common threshold. At the same time, the boundedness of all the signals in the closed-loop system can be guaranteed by using the average dwell time method. Moreover, the fault detection threshold is dependent on the average dwell time, which implies that the fault sensibility can be improved to a certain extent by selecting the average dwell time. Finally, a simulation example on the Chua's circuit system are employed to show the effectiveness of the proposed scheme.