A novel data-driven terminal iterative learning control for nonlinear time-varying systems

In this paper, a novel terminal iterative learning control approach is explored for a class of nonlinear discrete-time systems. It targets a terminal tracking tasks to a single desired point at the end of a run. A new control objective function is designed by incorporating a forgetting factor to obtain the control law. The proposed approach is a data-driven scheme and only depends on the measured I/O data without any modeling knowledge. The simulation results demonstrate the effectiveness of the proposed method.