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

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Samenvatting

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.

Originele taal-2Engels
Titel2024 IEEE International Conference on Robotics and Automation, ICRA 2024
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's11259-11265
Aantal pagina's7
ISBN van elektronische versie979-8-3503-8457-4
DOI's
StatusGepubliceerd - 8 aug. 2024
Evenement2024 IEEE International Conference on Robotics and Automation, ICRA 2024 - Yokohama, Japan
Duur: 13 mei 202417 mei 2024

Congres

Congres2024 IEEE International Conference on Robotics and Automation, ICRA 2024
Land/RegioJapan
StadYokohama
Periode13/05/2417/05/24

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