Iterative‐learning‐based actuator/sensor fault estimation for a class of repetitive nonlinear systems with time‐delay

Abstract To meet the demand for estimating actuator and sensor faults simultaneously in a class of repetitive nonlinear time‐delay systems, this paper proposes a novel fault estimation strategy based on an iterative learning scheme. Firstly, an iterative‐learning‐based fault estimation law is designed to estimate actuator faults while system is free of sensor failures. Both the fixed initial shift and random one are taken into consideration. Secondly, a novel sensor fault observer is proposed based on an augmented state variable which consists of original system state and sensor fault signal; output compensation strategy is also provided to ensure the iterative‐learning‐based actuator fault estimation method is effective considering the existence of sensor failures. In addition, theorems based on ‐norm and linear matrix inequality are provided to determine values or ranges of gain matrices and parameters in proposed sensor fault observer and iterative‐learning‐based actuator fault estimation law. Finally, two simulation examples are provided to illustrate the effectiveness of the proposed methods.