Optimal Data-Driven Difference-Inversion-Based Iterative Control: High-Speed Nanopositioning Tracking Example

Abstract

In this article, an optimal data-driven difference-inversion-based iterative control (ODDD-IIC) method is proposed for high-speed precision tracking in the presence of dynamics changes and random disturbances. Iterative learning control (ILC) has been shown to be advantageous over feedback and feedforward control for repetitive operations. Challenges, however, still exist to achieve high accuracy and fast convergence in ILCs as the bandwidth, i.e., the frequency range for guaranteed convergence, can be limited by adverse effects of modeling error and random disturbances. The aim of the proposed method is to compensate for these adverse effects through a data-driven approach without a modeling process. A frequency- and iteration-dependent iteration gain is introduced in the control law to enhance both the tracking performance and the robustness. The technique is illustrated in an output tracking experiment on a piezoelectric actuator, with comparison to two existing ILC methods.