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Learning-based control methods utilize run-time data from the underlyingprocess to improve the controller performance under model mismatch andunmodeled disturbances. This is beneficial for optimizing industrial processes,where the dynamics are difficult to model, and the repetitive nature of theprocess can be exploited. In this work, we develop an iterative approach forrepetitive precision motion control problems where the objective is to follow areference geometry with minimal tracking error. Our method utilizes a nominalmodel of the process and learns the mismatch using Gaussian Process Regression(GPR). The control input and the GPR data are updated after each iteration toimprove the performance in a run-to-run fashion. We provide a preliminaryconvergence analysis, implementation details of the proposed controller forminimizing different error types, and a case study where we demonstrateimproved tracking performance with simulation and experimental results.

Learning-Based Repetitive Precision Motion Control with Mismatch Compensation