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Vibration Suppression in Collaborative Flexible Payload Manipulation Using Passive Force Control

  • Rheinland-Pfälzische Technical University of Kaiserslautern (RPTU)
  • United Kingdom Atomic Energy Authority (UKAEA)
  • University of Oulu

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

Abstract

In large and heavy structures, vibrations arise during motion, posing significant challenges for precise manipulation. To accomplish the desired motion, control algorithms must effectively suppress these structural vibrations. In cutting-edge projects, such as remote maintenance of future fusion energy reactors (tokamaks), the manipulation of this type of structure is defined as a crucial task. This paper presents a control strategy to suppress transverse vibrations in flexible payloads during motion using a collaborative payload manipulation approach. Two different industrial robot arms are arranged in a leader-follower configuration for the manipulation strategy. The leader robot guides the motion with shaped velocity commands, while the follower robot ensures compliance with the estimated external forces applied by the leader on the payload through an admittance controller. Unlike existing methods, the proposed approach enables collaborative manipulation of heavier and larger flexible objects, addressing additional challenges such as vibration suppression and heterogeneous robot specifications. The dynamics of the leader-follower-payload system are modeled using an equivalent mass-spring-damper model, and it is shown that, with appropriate admittance parameters, the total energy of the system is passively dissipated. A stability proof is also provided. Numerical simulations validate the proposed method, and experimental results (see online video [1] ) demonstrate its effectiveness.

Original languageEnglish
Title of host publication2026 European Control Conference (ECC)
PublisherIEEE Institute of Electrical and Electronic Engineers
Pages176-181
Number of pages6
ISBN (Electronic)978-3-907144-13-8
Publication statusAccepted/In press - 2026
MoE publication typeA4 Article in a conference publication
Event2026 European Control Conference, ECC 2026 - Reykjavík, Iceland
Duration: 7 Jul 202610 Jul 2026

Conference

Conference2026 European Control Conference, ECC 2026
Country/TerritoryIceland
CityReykjavík
Period7/07/2610/07/26

Funding

This project has received funding from Research Council of Finland project UNITE: Unifying and optimizing representations in robot learning. This work has been carried out within the framework of the EUROfusion Consortium, funded by the European Union via the Euratom Research and Training Programme (Grant Agreement No 101052200 EUROfusion).

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy
  2. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure

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