Nie, Y. orcid.org/0000-0002-0688-413X and Kerrigan, E.C. orcid.org/0000-0002-3967-1544 (2025) Reliable solution to dynamic optimization problems using integrated residual regularized direct collocation. IEEE Control Systems Letters, 9. pp. 1063-1068. ISSN: 2475-1456
Abstract
Direct collocation (DC) is a widely used method for solving dynamic optimization problems (DOPs), but its implementation simplicity and computational efficiency are limited for challenging problems. For DOPs involving singular arcs, DC solutions often exhibit significant fluctuations along the singular arc, accompanied by large residual errors between collocation points, where the dynamic constraints are enforced as equality constraints. In this paper, we introduce the direct transcription method of integrated residual regularized direct collocation (IRRDC). This approach enforces dynamic constraints using a combination of point-wise residual constraints (expressed as either equalities or inequalities) and a penalty term on the integrated residual error, which helps reduce errors between collocation points. IRRDC retains the implementation simplicity of DC while improving both solution accuracy and efficiency, particularly for challenging problem types. Through the examples, we demonstrate that for problems where traditional DC results in excessive fluctuations, IRRDC effectively suppresses fluctuations and yields solutions with greater accuracy — at least two orders of magnitude lower in various error measures in relation to the dynamic and path constraints.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2025 The Authors. Except as otherwise noted, this author-accepted version of a journal article published in IEEE Control Systems Letters is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ |
Keywords: | Optimal control; numerical algorithms; predictive control for nonlinear systems |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > School of Electrical and Electronic Engineering |
Date Deposited: | 08 Oct 2025 10:06 |
Last Modified: | 08 Oct 2025 11:31 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/lcsys.2025.3580771 |
Related URLs: | |
Sustainable Development Goals: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:232661 |
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