Multirate Iterative Learning Control for Enhanced Motion Performance with Application to Wafer Scanner Systems

Iterative learning control (ILC) is an effective control technique for servo improvement in systems that repetitively execute the same tasks. In the learning process, the measured tracking error from the current iteration is incorporated to generate a new feedforward compensation signal to improve the system performance in the next iteration. Due to its discrete-time implementation, conventional ILC only considers errors at the sampled output points without inter-sample learning ability. Therefore, its achievable performance is limited by the output sampling rate. In this paper, a multirate ILC (MRILC) approach is proposed. Based on multirate Kalman Smoother and multirate feedforward control, the ILC update law in a multirate two-degree-of-freedom (2-DOF) control system with a fast feedforward ILC input but a slow output sampling rate is derived. The bandwidth of the learning loop may then be extended beyond that of the feedback loop for enhanced inter-sample learning. The effectiveness of the proposed MRILC is verified by experiments on a wafer scanner system.