A Bayesian Optimization Framework for the Automatic Tuning of MPC-based Shared Controllers

被引：0

作者：

van der Horst, Anne ^{[1
]}

Meere, Bas ^{[1
]}

Krishnamoorthy, Dinesh ^{[1
]}

Bakker, Saray ^{[1
,2
]}

van de Vrande, Bram ^{[3
]}

Stoutjesdijk, Henry ^{[3
]}

Alonso, Marco ^{[3
]}

Torta, Elena ^{[1
]}

机构：

[1] Eindhoven Univ Technol, Eindhoven, Netherlands

[2] Delft Univ Technol, Delft, Netherlands

[3] Philips IGT Syst Mechatron, Noord Brabant, Netherlands

来源：

2024 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2024) | 2024年

关键词：

D O I：

10.1109/ICRA57147.2024.10610655

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：11259 / 11265

页数：7

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