KPC: Learning-Based Model Predictive Control with Deterministic Guarantees

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dc.contributor.authorMaddalena, Emilio T
dc.contributor.authorScharnhorst, Paul
dc.contributor.authorJiang, Yuning
dc.contributor.authorJones, Colin N
dc.date.accessioned2024-02-20T10:21:27Z
dc.date.available2024-02-20T10:21:27Z
dc.date.issued2021
dc.description.abstractWe propose Kernel Predictive Control (KPC), a learning-based predictive control strategy that enjoys deterministic guarantees of safety. Noise-corrupted samples of the unknown system dynamics are used to learn several models through the formalism of non-parametric kernel regression. By treating each prediction step individually, we dispense with the need of propagating sets through highly non-linear maps, a procedure that often involves multiple conservative approximation steps. Finite-sample error bounds are then used to enforce state-feasibility by employing an efficient robust formulation. We then present a relaxation strategy that exploits online data to weaken the optimization problem constraints while preserving safety. Two numerical examples are provided to illustrate the applicability of the proposed control method.
dc.identifier.citationProceedings of the 3rd Conference on Learning for Dynamics and Control, PMLR 144:1015-1026
dc.identifier.urihttps://hdl.handle.net/20.500.12839/1352
dc.language.isoen
dc.titleKPC: Learning-Based Model Predictive Control with Deterministic Guarantees
dc.typeProceedings Article
dc.type.csemdivisionsBU-V
dc.type.csemresearchareasDigital Energy
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