Iterative learning control for spatially interconnected systems

Iterative learning control (ILC) has been successfully employed for trajectory tracking of uncertain dynamic systems with less system information. This paper attempts to adopt the benefits of ILC to improve the trajectory tracking performance of spatially interconnected systems. By utilizing the ILC update law along the iteration domain repetitively, a perfect reference trajectory tracking can be ensured. It is the key benefit of using ILC that less system model information is used in the design of a trajectory tracking controller for spatially interconnected systems. Through a numerical simulation, the validity of the proposed control scheme is illustrated.