Safe learning in nonlinear model predictive control

Johannes Buerger, Mark Cannon, Martin Doff-Sotta
Proceedings of the 6th Annual Learning for Dynamics & Control Conference, PMLR 242:603-614, 2024.

Abstract

A robust Model Predictive Control algorithm is proposed for learning-based control with model represented by an affine combination of basis functions. The online optimization is formulated as a sequence of convex programming problems derived by linearizing concave components of the dynamic model. A tube-based approach ensures satisfaction of constraints on control variables and model states while avoiding conservative bounds on linearization errors. The linear dependence of the model on unknown parameters is exploited to allow safe online parameter adaptation. The resulting algorithm is recursively feasible and provides closed loop stability and performance guarantees. Numerical examples are provided to illustrate the approach.

Cite this Paper

BibTeX
@InProceedings{pmlr-v242-buerger24a, title = {Safe learning in nonlinear model predictive control}, author = {Buerger, Johannes and Cannon, Mark and Doff-Sotta, Martin}, booktitle = {Proceedings of the 6th Annual Learning for Dynamics & Control Conference}, pages = {603--614}, year = {2024}, editor = {Abate, Alessandro and Cannon, Mark and Margellos, Kostas and Papachristodoulou, Antonis}, volume = {242}, series = {Proceedings of Machine Learning Research}, month = {15--17 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v242/buerger24a/buerger24a.pdf}, url = {https://proceedings.mlr.press/v242/buerger24a.html}, abstract = {A robust Model Predictive Control algorithm is proposed for learning-based control with model represented by an affine combination of basis functions. The online optimization is formulated as a sequence of convex programming problems derived by linearizing concave components of the dynamic model. A tube-based approach ensures satisfaction of constraints on control variables and model states while avoiding conservative bounds on linearization errors. The linear dependence of the model on unknown parameters is exploited to allow safe online parameter adaptation. The resulting algorithm is recursively feasible and provides closed loop stability and performance guarantees. Numerical examples are provided to illustrate the approach.} }
Endnote
%0 Conference Paper %T Safe learning in nonlinear model predictive control %A Johannes Buerger %A Mark Cannon %A Martin Doff-Sotta %B Proceedings of the 6th Annual Learning for Dynamics & Control Conference %C Proceedings of Machine Learning Research %D 2024 %E Alessandro Abate %E Mark Cannon %E Kostas Margellos %E Antonis Papachristodoulou %F pmlr-v242-buerger24a %I PMLR %P 603--614 %U https://proceedings.mlr.press/v242/buerger24a.html %V 242 %X A robust Model Predictive Control algorithm is proposed for learning-based control with model represented by an affine combination of basis functions. The online optimization is formulated as a sequence of convex programming problems derived by linearizing concave components of the dynamic model. A tube-based approach ensures satisfaction of constraints on control variables and model states while avoiding conservative bounds on linearization errors. The linear dependence of the model on unknown parameters is exploited to allow safe online parameter adaptation. The resulting algorithm is recursively feasible and provides closed loop stability and performance guarantees. Numerical examples are provided to illustrate the approach.
APA
Buerger, J., Cannon, M. & Doff-Sotta, M.. (2024). Safe learning in nonlinear model predictive control. Proceedings of the 6th Annual Learning for Dynamics & Control Conference, in Proceedings of Machine Learning Research 242:603-614 Available from https://proceedings.mlr.press/v242/buerger24a.html.

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