Auto-Tuning of Model Predictive Control for Bilateral Teleoperation with Bayesian Optimization

被引：0

作者：

Almasalmah, Fadi Alyousef ^{[1
]}

Omran, Hassan ^{[1
]}

Liu, Chao ^{[2
]}

Poignonec, Thibault ^{[1
]}

Bayle, Bernard ^{[1
]}

机构：

[1] Univ Strasbourg, ICube Lab, Strasbourg, France

[2] Univ Montpellier, CNRS, LIRMM, Montpellier, France

来源：

IFAC PAPERSONLINE | 2024年 / 58卷 / 30期

关键词：

Bilateral Teleoperation; Model Predictive Control; Controller Auto-Tuning; Bayesian Optimization; Physical Human-Robot Interaction; ALGORITHMS; LOOP;

D O I：

10.1016/j.ifacol.2025.01.161

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Model Predictive Control (MPC) is becoming a popular control method for teleoperation due to its ability to ensure safety constraints. However, tuning MPC is a non-intuitive process that requires significant expertise and effort. In this work, we propose a method for auto-tuning a model predictive controller in bilateral teleoperation settings. We use the Bayesian Optimization algorithm (BO) to seek the optimal weights of the MPC cost function for precise teleoperation. Our simulations and experiments show the effectiveness of the proposed tuning method. Copyright (c) 2024 The Authors.

引用

页码：85 / 90

页数：6

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