Data-driven iterative tuning based active disturbance rejection control for piezoelectric nano-positioners

Abstract

The nano scale motions of piezoelectric actuated nano-positioning systems are very sensitive to operating tasks, external disturbances, as well as variations of system dynamics. In this paper, a data-driven Iterative Feedback Tuning (IFT) based Active Disturbance Rejection Control (ADRC) approach is developed to optimize the control performance by conducting controller parameter tuning iteratively from experimental test data. In particular, a parameterized input-output form of the linear ADRC controller is derived for the nano-positioner, where the IFT algorithm is applied to solve the established optimization problem to get the optimal control parameters. The proposed method is verified in simulations where the selection of control criterion and the impact of update step-size are also discussed. Single-axis and dual-axes real-time experiments are finally conducted on an X-Y piezoelectric actuated nano-positioner, which demonstrate significant performance improvement on nano-positioning and tracking over the conventional ADRC method.