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Learning Approach to the Active Compliance Control of Multi-Arm Robots Coupled through a Flexible Object

Albrichsfeld, Christian von ; Svinin, Mikhail ; Tolle, Henning (2021)
Learning Approach to the Active Compliance Control of Multi-Arm Robots Coupled through a Flexible Object.
European Control Conference (ECC 95). Rome, Italy (05.-08.09.1995)
doi: 10.26083/tuprints-00019281
Conference or Workshop Item, Secondary publication, Publisher's Version

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Item Type: Conference or Workshop Item
Type of entry: Secondary publication
Title: Learning Approach to the Active Compliance Control of Multi-Arm Robots Coupled through a Flexible Object
Language: English
Date: 2021
Place of Publication: Darmstadt
Book Title: Proceedings of the third European Control Conference
Collation: 6 ungezählte Seiten
Event Title: European Control Conference (ECC 95)
Event Location: Rome, Italy
Event Dates: 05.-08.09.1995
DOI: 10.26083/tuprints-00019281
Origin: Secondary publication service
Abstract:

This paper presents a quasi-static model and a control strategy for N robot arms cooperating through a concerning its compliant behaviour partly unknown flexible object. The control strategy is based on the position/force decomposition of an extended 6N-dimensional space. The strategy includes feedforward and feedback levels. The Feedback level is organized in the form of an active compliance control law. An AMS-based learning approach is used to accommodate the compliance behaviour of the system and utilized as an additional feedforward loop in the control system. The applicability of the control strategy is verified by simulation.

Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-192810
Additional Information:

Erscheint in: Volume 3, part 1

Classification DDC: 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
Divisions: 18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik > Control Methods and Robotics (from 01.08.2022 renamed Control Methods and Intelligent Systems)
Date Deposited: 09 Sep 2021 13:07
Last Modified: 09 Aug 2023 12:25
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/19281
PPN: 486177300
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