Performance Residual Based Fault Detection for Feedback Control Systems

Abstract

This brief is devoted to the issues of performance supervised fault detection (FD) for two types of feedback control systems by adopting the novel performance residual as the process evaluator. Specifically, for a class of uncertain state feedback control systems, the boundaries of the performance residual caused by the model uncertainties are analyzed theoretically, then the randomized algorithm is exploited to construct the threshold, aiming at avoiding the worst-case handling and improving the FD performance. The second part considers the general dynamic output feedback control systems with external reference signals. For the FD purpose, the performance residual is derived based on the backward computation, and the state variables of the closed-loop system are constructed by the accessible input, output, and reference signals to release the strict requirements for full state measurements. Finally, the effectiveness of the developed FD approaches is demonstrated on the model of a DC motor system.