Auto-Tuning of Model Predictive Control for Bilateral Teleoperation with Bayesian Optimization - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier
Conference Papers Year : 2024

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

Abstract

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 nonintuitive 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.
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Dates and versions

lirmm-04795634 , version 1 (21-11-2024)

Identifiers

  • HAL Id : lirmm-04795634 , version 1

Cite

Fadi Alyousef Almasalmah, Hassan Omran, Chao Liu, Thibault Poignonec, Bernard Bayle. Auto-Tuning of Model Predictive Control for Bilateral Teleoperation with Bayesian Optimization. CPHS 2024 - 5th IFAC Workshop on Cyber-Physical and Human Systems, Dec 2024, Antalya (TR), Turkey. ⟨lirmm-04795634⟩
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