PhD Candidate in Information Technology – Systems and Control at Politecnico di Milano
ODYS Srl
Kristoffer Løwenstein 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.
Project description
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.
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.
Publications
QPALM-OCP: A Newton-type Proximal Augmented Lagrangian tailored for Quadratic Programs arising in Model Predictive Control Journal Article
In: IEEE Control Systems Letters, 2024, ISSN: 2475-1456, (Submitted to the 63rd IEEE Conference on Decision and Control 2024 (CDC)).
Physics-Informed Online Learning by Moving Horizon Estimation: Learning Recurrent Neural Networks in Gray-box Models Proceedings Forthcoming
Forthcoming, (Accepted to the 2024 IFAC Conference on Nonlinear Model Predictive Control (NMPC)).
Obstacle avoidance for a robotic manipulator with linear-quadratic Model Predictive Control Proceedings Article Forthcoming
In: 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE 2024), Forthcoming.
Physics-Informed Online Learning of Gray-box Models by Moving Horizon Estimation Journal Article
In: European Journal of Control, pp. 100861, 2023, ISSN: 0947-3580.