Large Dynamic Range Nanopositioning Using Iterative Learning Control

This paper presents the control system design and tracking performance of a large range single-axis nanopositioning system that is based on a moving magnet actuator and flexure bearing. While the physical system is designed to be free of friction and backlash, the nonlinearities in the electromagnetic actuator as well as the harmonic distortion in the drive amplifier restrict the achievable tracking performance for dynamic command following. It is shown that linear feedback proves to be inadequate due to limitations arising from the low open-loop bandwidth of the physical system. For periodic commands, like those used in scanning applications, the component of the tracking error due to the nonlinearities is deterministic and repeats every period. Therefore, an iterative learning controller (ILC) is designed and implemented in conjunction with linear feedback to reduce this periodic tracking error by more than three orders of magnitude. Experimental results demonstrate the effectiveness of this ILC in achieving 18nm RMS tracking error over 6mm range in response to a 2Hz band-limited triangular command. This corresponds to a dynamic range of 10⁵.