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Conference Papers Year : 2023

Maintaining a relevant dataset for data-driven MPC using Willems' fundamental lemma extensions

Abstract

This work explores the recent formulation of nonlinear Data-driven Model Predictive Control in the case of dynamic references. Indeed, the state-of-the-art methods rely on Willems fundamental lemma, and freeze the used dataset at some point. While this ensures consistent behavior, i.e., excitation and accuracy, for a given setpoint, this will likely fail when the reference, and thus the operating point, changes. To this end, we propose refined heuristics for dataset management. First, a singular value-based method induces regular dataset updates but still guarantees a minimum excitation level. Then, a double-dataset formulation aims at decoupling accuracy and excitation issues and leverages the singular value-based one. These heuristics are validated in real-time experiments on a heat-blower system.
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Dates and versions

hal-04073063 , version 1 (18-04-2023)
hal-04073063 , version 2 (30-08-2023)

Identifiers

Cite

Alexandre Faye-Bédrin, Stanislav Aranovskiy, Paul Chauchat, Romain Bourdais. Maintaining a relevant dataset for data-driven MPC using Willems' fundamental lemma extensions. 2023 62nd IEEE Conference on Decision and Control (CDC), Dec 2023, Singapore, Singapore. pp.2584-2589, ⟨10.1109/CDC49753.2023.10383545⟩. ⟨hal-04073063v2⟩
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