Robust Optimal ILC Design for Precision Servo: Application to an XY Table

Abstract

This chapter addresses the high-precision servo-control problem using the iterative learning control techniques. Instead of seeking an accurate model of servomechanism, servo tracking performance is enhanced via repeated learning. To facilitate the sampled learning control system design, we propose two optimal objective functions. The first objective function is to maximize the frequency range in which learning converges, and subsequetly enhance the system robustness. The second objective function is to search the fastest learning convergence speed in the iteration domain. Under a unified optimal design, we then compare three representative iterative learning control algorithms PCL, CCL and PCCL, exploit their suitability for the servo implementation. We further elaborate on the issue of robust optimal design, which seeks the fastest learning convergence under the worst-case parametric uncertainties. The experimental results show that the high-precision tracking performance is achieved via iterative learning, despite the existence of miscellaneous non-linear and uncertain factors.