{"id":717,"date":"2021-08-30T20:49:37","date_gmt":"2021-08-30T20:49:37","guid":{"rendered":"https:\/\/elo-x.eu\/?p=717"},"modified":"2023-12-31T11:06:37","modified_gmt":"2023-12-31T11:06:37","slug":"kristoffer-lowenstein","status":"publish","type":"post","link":"https:\/\/elo-x.eu\/?p=717","title":{"rendered":"Kristoffer L\u00f8wenstein"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\t\t\t\n\t\t\t\tKristoffer L\u00f8wenstein \n\t\t\t<\/h2 > \n\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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PhD Candidate in Information Technology \u2013 Systems and Control at Politecnico di Milano<\/span><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Kristoffer L\u00f8wenstein graduated from Aarhus University with a degree in mechanical engineering in January 2021. He worked with soft robotics in his thesis. His major research interests lie within dynamics, robotics, controls, artificial Intelligence, and the interplay between these fields. Kristoffer has carried out exchange semesters at Royal Melbourne Institute of Technology and Politecnico di Milano. Besides his studies, he played basketball at Bakken Bears winning multiple national championships in Denmark.\u00a0\u00a0<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Project description<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Next-generation embedded control systems will have to cope with tight constraints on computation and storage resources, while at the same time satisfying increasingly challenging requirements on performance quality, energy consumption, safety, emissions, etc. In this context, model-based control techniques such as MPC are envisioned to play a key role, by providing a systematic framework to minimize a desired objective function under constraints. Real application of online MPC to high-volume automotive production programs and other industrial applications has only very recently started to blossom and will likely continue to spread in the next years, but developments in the field of learning-based MPC, i.e. novel techniques nonlinear MPC incorporating machine learning techniques, is expected to accelerate the use even further.<\/p>\n

This research project focuses on the development of novel physics-informed methods for learning and adapting prediction models for embedded nonlinear MPC. By exploiting physical knowledge in the training process learning-based components such as neural networks of rather limited size can be used to capture model mismatches. The developed algorithms aims at easing the design of MPC controllers and improve their performance, by reducing calibration time before production and adapting calibration parameters at run-time. Further, tailored optimization algorithms for solving the underlying numerical programs are investigated to enable real-time embedded applications of the proposed learning-based MPC methods.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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1.<\/div>

Pas, Pieter; L\u00f8wenstein, Kristoffer Fink; Bernardini, Daniele; Patrinos, Panagiotis<\/p>

Exploiting Parallelism in a QPALM-based Solver for Optimal Control Workshop<\/span> <\/p>

2024<\/span>, (RSS 2024 workshop: Frontiers of optimization for robotics)<\/span>.<\/p>

BibTeX<\/a><\/span><\/p>

@workshop{PasQPALMOCP,
\r\ntitle = {Exploiting Parallelism in a QPALM-based Solver for Optimal Control },
\r\nauthor = {Pieter Pas and Kristoffer Fink L\u00f8wenstein and Daniele Bernardini and Panagiotis Patrinos},
\r\nyear  = {2024},
\r\ndate = {2024-07-14},
\r\nurldate = {2024-07-14},
\r\nnote = {RSS 2024 workshop: Frontiers of optimization for robotics},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {workshop}
\r\n}
\r\n<\/pre><\/div>
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2.<\/div>
 L\u00f8wenstein, Kristoffer Fink;  Bernardini, Daniele;  Patrinos, Panagiotis<\/p>
QPALM-OCP: A Newton-type Proximal Augmented Lagrangian tailored for Quadratic Programs arising in Model Predictive Control<\/a> Journal Article<\/span> <\/p>
In: <\/span>IEEE Control Systems Letters, <\/span>2024<\/span>, ISSN: 2475-1456<\/span>, (Submitted to the 63rd IEEE Conference on Decision and Control 2024 (CDC))<\/span>.<\/p>
Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
@article{Lowenstein2024QPALMOCP,
\r\ntitle = {QPALM-OCP: A Newton-type Proximal Augmented Lagrangian tailored for Quadratic Programs arising in Model Predictive Control},
\r\nauthor = {Kristoffer Fink L\u00f8wenstein and Daniele Bernardini and Panagiotis Patrinos},
\r\ndoi = {10.1109\/LCSYS.2024.3410638},
\r\nissn = {2475-1456},
\r\nyear  = {2024},
\r\ndate = {2024-06-06},
\r\nurldate = {2024-04-02},
\r\njournal = {IEEE Control Systems Letters},
\r\nabstract = {In Model Predictive Control (MPC) fast and reliable Quadratic Programming (QP) solvers are of fundamental importance. The inherent structure of the subsequent Optimal Control Problems (OCPs) can lead to substantial performance improvements if exploited. Therefore, we present a structure-exploiting proximal augmented Lagrangian based solver extending the general-purpose QP-solver QPALM. Our solver relies on semismooth Newton iterates to solve the inner sub-problem while directly accounting for the OCP structure via efficient and sparse matrix factorizations. The matrices to be factorized depends on the active set and therefore low-rank factorization updates can be employed like in active-set methods resulting in cheap iterates. We benchmark our solver against other state-of-the-art QP-solvers and our algorithm compare favorably against these solvers},
\r\nnote = {Submitted to the 63rd IEEE Conference on Decision and Control 2024 (CDC)},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
\r\n<\/pre><\/div>
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In Model Predictive Control (MPC) fast and reliable Quadratic Programming (QP) solvers are of fundamental importance. The inherent structure of the subsequent Optimal Control Problems (OCPs) can lead to substantial performance improvements if exploited. Therefore, we present a structure-exploiting proximal augmented Lagrangian based solver extending the general-purpose QP-solver QPALM. Our solver relies on semismooth Newton iterates to solve the inner sub-problem while directly accounting for the OCP structure via efficient and sparse matrix factorizations. The matrices to be factorized depends on the active set and therefore low-rank factorization updates can be employed like in active-set methods resulting in cheap iterates. We benchmark our solver against other state-of-the-art QP-solvers and our algorithm compare favorably against these solvers<\/div>
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