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EigenMPC: An Eigenmanifold-Inspired Model-Predictive Control Framework for Exciting Efficient Oscillations in Mechanical Systems

Coelho, Andre and Albu-Schäffer, Alin and Sachtler, Arne and Hrishik, Mishra and Bicego, Davide and Ott, Christian and Franchi, Antonio (2023) EigenMPC: An Eigenmanifold-Inspired Model-Predictive Control Framework for Exciting Efficient Oscillations in Mechanical Systems. In: 61st IEEE Conference on Decision and Control, CDC 2022, pp. 2437-2442. IEEE. 2022 IEEE 61st Conference on Decision and Control (CDC), 06-09 Dec 2022, Cancún, Mexico. doi: 10.1109/CDC51059.2022.9992915. ISBN 978-1-6654-6761-2. ISSN 0743-1546.

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Official URL: https://ieeexplore.ieee.org/document/9992915

Abstract

This paper proposes a Nonlinear Model-Predictive Control (NMPC) method capable of finding and converging to energy-efficient regular oscillations, which require no control action to be sustained. The approach builds up on the recently developed Eigenmanifold theory, which defines the sets of line-shaped oscillations of a robot as an invariant two-dimensional submanifold of its state space. By defining the control problem as a nonlinear program (NLP), the controller is able to deal with constraints in the state and control variables and be energy-efficient not only in its final trajectory but also during the convergence phase. An initial implementation of this approach is proposed, analyzed, and tested in simulation.

Item URL in elib:https://elib.dlr.de/193250/
Document Type:Conference or Workshop Item (Speech)
Title:EigenMPC: An Eigenmanifold-Inspired Model-Predictive Control Framework for Exciting Efficient Oscillations in Mechanical Systems
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Coelho, AndreUNSPECIFIEDhttps://orcid.org/0000-0002-0917-5574UNSPECIFIED
Albu-Schäffer, AlinUNSPECIFIEDhttps://orcid.org/0000-0001-5343-9074142115924
Sachtler, ArneUNSPECIFIEDhttps://orcid.org/0000-0003-4974-4134UNSPECIFIED
Hrishik, MishraUNSPECIFIEDhttps://orcid.org/0000-0002-5025-2447UNSPECIFIED
Bicego, DavideUNSPECIFIEDhttps://orcid.org/0000-0002-3423-0969UNSPECIFIED
Ott, ChristianUNSPECIFIEDhttps://orcid.org/0000-0003-0987-7493UNSPECIFIED
Franchi, AntonioUNSPECIFIEDhttps://orcid.org/0000-0002-5670-1282UNSPECIFIED
Date:10 January 2023
Journal or Publication Title:61st IEEE Conference on Decision and Control, CDC 2022
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:Yes
DOI:10.1109/CDC51059.2022.9992915
Page Range:pp. 2437-2442
Publisher:IEEE
ISSN:0743-1546
ISBN:978-1-6654-6761-2
Status:Published
Keywords:Analytical models, Aerospace electronics, Energy efficiency, Trajectory, Mechanical Systems, Oscillators, Robots
Event Title:2022 IEEE 61st Conference on Decision and Control (CDC)
Event Location:Cancún, Mexico
Event Type:international Conference
Event Dates:06-09 Dec 2022
Organizer:IEEE
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Robotics
DLR - Research area:Raumfahrt
DLR - Program:R RO - Robotics
DLR - Research theme (Project):R - Robot Dynamics & Simulation [RO]
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013) > Analysis and Control of Advanced Robotic Systems
Institute of Robotics and Mechatronics (since 2013)
Deposited By: Sachtler, Arne
Deposited On:16 Jan 2023 16:56
Last Modified:27 Oct 2023 15:30

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