Iterative Learning Fault-tolerant Control for a Class of Uncertain Nonlinear Discrete Repetitive Processes

For a class of uncertain discrete repetitive systems with nonlinearity and actuator faults, we propose an iterative learning fault-tolerant control algorithm. First, we transform the iterative learning control process into an equivalent uncertain nonlinear repetitive process model by defining an actuator fault coefficient matrix. Then, we discuss the stability of the nonlinear repetitive process on time and trial axes, based on the mixed Lyapunov function method, and provide the sufficient condition for the stability of the repetitive control system in terms of the linear matrix inequality technology. The fault-tolerant performance of the system can be guaranteed both in normal and failure actuator conditions. The simulation results of the output tracking of a single-link manipulator system reveal the effectiveness of the algorithm.