Adaptive learning-based model predictive control for uncertain interconnected systems: A set membership identification approach
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Date
2025-01
Publication Type
Journal Article
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yes
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Abstract
We propose a novel adaptive learning-based model predictive control (MPC) scheme for interconnected systems which can be decomposed into several smaller dynamically coupled subsystems with uncertain coupling. The proposed scheme is mainly divided into two main online phases; a learning phase and an adaptation phase. Set membership identification is used in the learning phase to learn an uncertainty set that contains the coupling strength using online data. In the adaptation phase, rigid tube-based robust MPC is used to compute the optimal predicted states and inputs. Besides computing the optimal trajectories, the MPC ingredients are adapted in the adaptation phase taking the learnt uncertainty set into account. These MPC ingredients include the prestabilizing controller, the rigid tube, the tightened constraints and the terminal ingredients. The recursive feasibility of the proposed scheme as well as the stability of the corresponding closed-loop system are discussed. The developed scheme is compared in simulations to existing schemes including robust, adaptive and learning-based MPC.
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published
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Volume
171
Pages / Article No.
111943
Publisher
Elsevier
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Subject
Model predictive control; Optimization-based control; Learning-based control
Organisational unit
02650 - Institut für Automatik / Automatic Control Laboratory