A Bayesian optimization framework for the automatic tuning of MPC-based shared controllers

Anne van der Horst; Bas Meere; Dinesh Krishnamoorthy; Saray Bakker; Bram van de Vrande; Henry Stoutjesdijk; Marco Alonso; Elena Torta

arXiv · 2311.01133

A Bayesian optimization framework for the automatic tuning of MPC-based shared controllers

Anne van der Horst·Bas Meere·Dinesh Krishnamoorthy·Saray Bakker·Bram van de Vrande·Henry Stoutjesdijk·Marco Alonso·Elena Torta

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Abstract

This paper presents a Bayesian optimization framework for the automatic tuning of shared controllers which are defined as a Model Predictive Control (MPC) problem. The proposed framework includes the design of performance metrics as well as the representation of user inputs for simulation-based optimization. The framework is applied to the optimization of a shared controller for an Image Guided Therapy robot. VR-based user experiments confirm the increase in performance of the automatically tuned MPC shared controller with respect to a hand-tuned baseline version as well as its generalization ability.

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Anne van der Horst, Bas Meere, Dinesh Krishnamoorthy, Saray Bakker, Bram van de Vrande, Henry Stoutjesdijk, Marco Alonso, Elena Torta. 2023-11-02. A Bayesian optimization framework for the automatic tuning of MPC-based shared controllers. https://arxiv.org/abs/2311.01133

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