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

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

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

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arxiv 2311.01133 v1 pith:C54OOZEM submitted 2023-11-02 cs.RO

Anne van der Horst , Bas Meere , Dinesh Krishnamoorthy , Saray Bakker , Bram van de Vrande , Henry Stoutjesdijk , Marco Alonso , Elena Torta This is my paper · ORCID

classification cs.RO

keywords frameworkoptimizationsharedautomaticbayesiancontrollercontrollersperformance

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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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