Actuator Fault Diagnosis for a Class of Nonlinear Uncertain Systems via Deterministic Learning

In this article, we investigate the actuator fault detection (AFD) problem for a class of closed-loop systems with nonlinear uncertain dynamics. Two AFD schemes are developed via the deterministic learning method (DLM). In the first scheme, knowledge of the system dynamics and actuator fault dynamics is extracted from state signals by using DLM. The learned knowledge is utilized to construct a bank of estimators. When the residual norm of a fault estimator becomes smaller than that of the nominal estimator, the occurrence of the actuator fault is deduced. In the second scheme, a learning controller is constructed to identify the feedback control dynamics under normal and fault conditions. The knowledge of the feedback control dynamics is utilized to construct a bank of controllers, which are embedded in a bank of estimators. When the residual norm of the estimator embedded with the matched controller becomes smaller than that of the estimator embedded with the nominal controller, the actuator fault can be detected. Finally, the properties of constant neural networks and the persistent excitation condition are exploited to derive the detectability conditions for diagnosis schemes. The attraction of this article is that two knowledge-bank-based AFD schemes are developed, in which the knowledge extracted from state and controller signals will enhance the sensitivity to actuator faults. Simulation results are also included to illustrate the effectiveness of these schemes.