An optimization-based method for improving the productivity of precision machine tools is proposed, where the reference path is computed in local coordinates, and information about the machine tool performance is learned from experimental data. The optimization yields a modified reference that is tracked by the existing low-level controller. The method is tested in simulation for a biaxial positioning system. The positioning system is modelled as double integrator, and the controller characteristic is modelled from experimental data using a least-squares fit. Simulation results show that the method is effective in designing optimal references even for challenging geometries such as sharp corners. The application of this procedure allows the retrofit of the control of existing machines with minimal overhead, by providing a modified reference file to track.

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We propose an optimization-based method to improve contour tracking performance on precision motion stages by modifying the reference trajectory, without changing the built-in low-level controller. The position of the precision motion stage is predicted with data-driven models. First, a linear lowfidelity model is used to optimize traversal time, by changing the path velocity and acceleration profiles. Second, a non-linear high-fidelity model is used to refine the previously found timeoptimal solution. We experimentally demonstrate that the method is capable of improving the productivity vs. accuracy trade-off for a high precision motion stage. Given the data-based nature of the models used, we claim that the method can easily be adapted to a wide family of precision motion systems.