Adaptive data-driven design of fault-tolerant control systems with unknown dynamics

Abstract

This paper investigates the adaptive data-driven design issue for fault-tolerant control systems with unknown dynamics. Initially, the fault-tolerant control problem is transformed into a stabilization problem for switched systems, where both the switching signal and system dynamics are unknown due to the uncertainties in fault occurrence instants and faulty modes. While extensive research has been conducted on switched systems, the strategies for addressing unknown switching signals remain comparatively scarce, especially when system dynamics are also unknown. To tackle this issue, a Lyapunov function-based monitoring scheme is provided to determine the time instants of switching in system dynamics during operation. Subsequently, a data-driven adaptive learning control mechanism is introduced to update feedback gains. Considering the asynchronous issue between the switching of the controller and system dynamics due to the learning process, sufficient conditions concerning the switching frequency of the system dynamics are provided. Thereby, a data-driven adaptive learning fault-tolerant control algorithm is proposed. Under the frequency constraint on the switching of system dynamics, it is shown that the offered control scheme maintains the closed-loop system’s stability. Finally, two simulation examples are provided to show the effectiveness of the proposed approach.