Data-driven Reference Trajectory Optimization for Precision Motion Systems

TL;DR

This paper introduces a data-driven, optimization-based pre-compensation method that enhances the accuracy and productivity of precision motion stages by refining reference trajectories without altering existing low-level controllers.

Contribution

The paper presents a novel data-driven approach combining linear and nonlinear models to optimize reference trajectories for precision motion systems, improving performance without modifying controllers.

Findings

Improved contour tracking accuracy demonstrated experimentally.

Enhanced productivity through optimized traversal time.

Method adaptable to various precision motion systems.

Abstract

We propose a data-driven optimization-based pre-compensation method to improve the contour tracking performance of precision motion stages by modifying the reference trajectory and without modifying any built-in low-level controllers. The position of the precision motion stage is predicted with data-driven models, a linear low-fidelity model is used to optimize traversal time, by changing the path velocity and acceleration profiles then a non-linear high-fidelity model is used to refine the previously found time-optimal solution. We experimentally demonstrate that the proposed method is capable of simultaneously improving the productivity and accuracy of a high precision motion stage. Given the data-based nature of the models, the proposed method can easily be adapted to a wide family of precision motion systems.