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

Andre Coelho; Alin Albu-Schaeffer; Arne Sachtler; Hrishik Mishra; Davide Bicego; Christian Ott; Antonio Franchi

doi:10.1109/CDC51059.2022.9992915

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

Andre Coelho, Alin Albu-Schaeffer, Arne Sachtler, Hrishik Mishra, Davide Bicego, Christian Ott, Antonio Franchi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

2 Citations (Scopus)

117 Downloads (Pure)

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.

Original language	English
Title of host publication	2022 IEEE 61st Conference on Decision and Control (CDC)
Publisher	IEEE
Pages	2437-2442
Number of pages	6
ISBN (Electronic)	978-1-6654-6761-2
ISBN (Print)	978-1-6654-6762-9
DOIs	https://doi.org/10.1109/CDC51059.2022.9992915
Publication status	Published - 10 Jan 2023
Event	61st IEEE Conference on Decision and Control, CDC 2022 - Cancun, Mexico Duration: 6 Dec 2022 → 9 Dec 2022 Conference number: 61

Conference

Conference	61st IEEE Conference on Decision and Control, CDC 2022
Abbreviated title	CDC
Country/Territory	Mexico
City	Cancun
Period	6/12/22 → 9/12/22

Keywords

Analytical models
Aerospace electronics
Energy efficiency
Trajectory
Mechanical systems
Oscillators
Robots
2023 OA procedure

Access to Document

10.1109/CDC51059.2022.9992915

ArticleFinal published version, 1.48 MBLicence: Taverne

Supplementary video to paper "EigenMPC: An Eigenmanifold-Inspired Model-Predictive Control Framework for Exciting Efficient Oscillations in Mechanical Systems"
Coelho, A. (Creator), 4TU.Centre for Research Data, 22 Feb 2022
DOI: 10.4121/19196765, https://data.4tu.nl/articles/_/19196765 and 2 more links, https://data.4tu.nl/articles/_/19196765/1, https://doi.org/10.4121/19196765.v1 (show fewer)
Dataset

2 Citations
1 Preprint

EigenMPC: An Eigenmanifold-Inspired Model-Predictive Control Framework for Exciting Efficient Oscillations in Mechanical Systems
Coelho, A., Albu-Schaeffer, A., Sachtler, A., Mishra, H., Bicego, D., Ott, C. & Franchi, A., 3 Mar 2023, ArXiv.org.
Research output: Working paper › Preprint › Academic

Open Access
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52 Downloads (Pure)

Cite this

Coelho, A., Albu-Schaeffer, A., Sachtler, A., Mishra, H., Bicego, D., Ott, C., & Franchi, A. (2023). EigenMPC: An Eigenmanifold-Inspired Model-Predictive Control Framework for Exciting Efficient Oscillations in Mechanical Systems. In 2022 IEEE 61st Conference on Decision and Control (CDC) (pp. 2437-2442). Article 9992915 IEEE. https://doi.org/10.1109/CDC51059.2022.9992915

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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.",

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Coelho, A, Albu-Schaeffer, A, Sachtler, A, Mishra, H, Bicego, D, Ott, C & Franchi, A 2023, EigenMPC: An Eigenmanifold-Inspired Model-Predictive Control Framework for Exciting Efficient Oscillations in Mechanical Systems. in 2022 IEEE 61st Conference on Decision and Control (CDC)., 9992915, IEEE, pp. 2437-2442, 61st IEEE Conference on Decision and Control, CDC 2022, Cancun, Mexico, 6/12/22. https://doi.org/10.1109/CDC51059.2022.9992915

EigenMPC: An Eigenmanifold-Inspired Model-Predictive Control Framework for Exciting Efficient Oscillations in Mechanical Systems. / Coelho, Andre; Albu-Schaeffer, Alin; Sachtler, Arne et al.
2022 IEEE 61st Conference on Decision and Control (CDC). IEEE, 2023. p. 2437-2442 9992915.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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N2 - 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.

AB - 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.

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

Abstract

Conference

Keywords

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Datasets

Supplementary video to paper "EigenMPC: An Eigenmanifold-Inspired Model-Predictive Control Framework for Exciting Efficient Oscillations in Mechanical Systems"

Research output

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

Cite this