Adaptive Robust Model Predictive Control for Bilateral Teleoperation - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Conference Papers Year : 2023

Adaptive Robust Model Predictive Control for Bilateral Teleoperation

Abstract

In this work, we use recent developments in the field of adaptive robust Model Predictive Control (MPC) to build a controller for bilateral teleoperation systems. To guarantee robust constraint satisfaction, we incorporate polytopic tube controllers in the MPC design. In addition, we use online learning methods to learn the environment model. Namely, we use set membership learning to learn the parametric uncertainty bounds and reduce the conservatism of the robust controller, and we combine it with least mean square method to learn a point estimate of the model parameters, which enhances the controller performance. Our simulation demonstrates the effectiveness of the proposed approach in maintaining robust constraint satisfaction and enhancing performance by learning during teleoperation tasks.

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Dates and versions

lirmm-04287699 , version 1 (15-11-2023)

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  • HAL Id : lirmm-04287699 , version 1

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Fadi Alyousef, Hassan Omran, Chao Liu, Bernard Bayle. Adaptive Robust Model Predictive Control for Bilateral Teleoperation. IROS 2023 - IEEE/RSJ International Conference on Intelligent Robots and Systems, Oct 2023, Detroit, United States. ⟨lirmm-04287699⟩
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