Choose Wisely: Data-driven Predictive Control for Nonlinear Systems Using Online Data Selection

Joshua Näf, Keith Moffat, Jaap Eising, Florian Dorfler
Proceedings of The 8th Annual Learning for Dynamics and Control Conference, PMLR 331:1895-1915, 2026.

Abstract

This paper proposes Select-Data-driven Predictive Control (Select-DPC), a new method for controlling nonlinear systems using output-feedback for which data are available but an explicit model is not. At each timestep, Select-DPC employs only the most relevant data to implicitly linearize the dynamics in “trajectory space.” Then, taking user-defined output constraints into account, it makes control decisions using a convex optimization. This data-driven optimal control is applied in a receding-horizon manner. As the online data-selection is the core of Select-DPC, we propose and compare norm-based and manifold-embedding-based data selection methods. We evaluate Select-DPC on three benchmark nonlinear system simulators—rocket-landing, a robotic arm, and cart-pole inverted pendulum swing-up—comparing them with standard Data-enabled Predictive Control (DeePC) and Time-Windowed DeePC methods, and find that Select-DPC outperforms both methods.

Cite this Paper

BibTeX
@InProceedings{pmlr-v331-naf26a, title = {Choose Wisely: Data-driven Predictive Control for Nonlinear Systems Using Online Data Selection}, author = {N\"af, Joshua and Moffat, Keith and Eising, Jaap and Dorfler, Florian}, booktitle = {Proceedings of The 8th Annual Learning for Dynamics and Control Conference}, pages = {1895--1915}, year = {2026}, editor = {Sukhatme, Gaurav and Lindemann, Lars and Tu, Stephen and Wierman, Adam and Atanasov, Nikolay}, volume = {331}, series = {Proceedings of Machine Learning Research}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v331/main/assets/naf26a/naf26a.pdf}, url = {https://proceedings.mlr.press/v331/naf26a.html}, abstract = {This paper proposes Select-Data-driven Predictive Control (Select-DPC), a new method for controlling nonlinear systems using output-feedback for which data are available but an explicit model is not. At each timestep, Select-DPC employs only the most relevant data to implicitly linearize the dynamics in “trajectory space.” Then, taking user-defined output constraints into account, it makes control decisions using a convex optimization. This data-driven optimal control is applied in a receding-horizon manner. As the online data-selection is the core of Select-DPC, we propose and compare norm-based and manifold-embedding-based data selection methods. We evaluate Select-DPC on three benchmark nonlinear system simulators—rocket-landing, a robotic arm, and cart-pole inverted pendulum swing-up—comparing them with standard Data-enabled Predictive Control (DeePC) and Time-Windowed DeePC methods, and find that Select-DPC outperforms both methods.} }
Endnote
%0 Conference Paper %T Choose Wisely: Data-driven Predictive Control for Nonlinear Systems Using Online Data Selection %A Joshua Näf %A Keith Moffat %A Jaap Eising %A Florian Dorfler %B Proceedings of The 8th Annual Learning for Dynamics and Control Conference %C Proceedings of Machine Learning Research %D 2026 %E Gaurav Sukhatme %E Lars Lindemann %E Stephen Tu %E Adam Wierman %E Nikolay Atanasov %F pmlr-v331-naf26a %I PMLR %P 1895--1915 %U https://proceedings.mlr.press/v331/naf26a.html %V 331 %X This paper proposes Select-Data-driven Predictive Control (Select-DPC), a new method for controlling nonlinear systems using output-feedback for which data are available but an explicit model is not. At each timestep, Select-DPC employs only the most relevant data to implicitly linearize the dynamics in “trajectory space.” Then, taking user-defined output constraints into account, it makes control decisions using a convex optimization. This data-driven optimal control is applied in a receding-horizon manner. As the online data-selection is the core of Select-DPC, we propose and compare norm-based and manifold-embedding-based data selection methods. We evaluate Select-DPC on three benchmark nonlinear system simulators—rocket-landing, a robotic arm, and cart-pole inverted pendulum swing-up—comparing them with standard Data-enabled Predictive Control (DeePC) and Time-Windowed DeePC methods, and find that Select-DPC outperforms both methods.
APA
Näf, J., Moffat, K., Eising, J. & Dorfler, F.. (2026). Choose Wisely: Data-driven Predictive Control for Nonlinear Systems Using Online Data Selection. Proceedings of The 8th Annual Learning for Dynamics and Control Conference, in Proceedings of Machine Learning Research 331:1895-1915 Available from https://proceedings.mlr.press/v331/naf26a.html.

Related Material