Distributed Model Predictive Control for Tracking: A Coalitional Clustering Approach

In this article, a coalitional robust model predictive controller for tracking target sets is presented. The overall system is controlled by a set of local control agents that dynamically merge into cooperative coalitions or clusters so as to attain an efficient tradeoff between cooperation burden a...

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Bibliographic Details
Published in:IEEE transactions on automatic control 2022-12, Vol.67 (12), p.6873-6880
Main Authors: Chanfreut, Paula, Maestre, J. M., Ferramosca, Antonio, Muros, Francisco Javier, Camacho, Eduardo F.
Format: Article
Language:English
Subjects:
Closed loops
Clustering
Coalitional model predictive control
control by clustering
Couplings
Feedback control
Network topology
Predictive control
Robust control
Subsystems
System dynamics
Target tracking
Topology
tracking
Tracking control
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Summary:In this article, a coalitional robust model predictive controller for tracking target sets is presented. The overall system is controlled by a set of local control agents that dynamically merge into cooperative coalitions or clusters so as to attain an efficient tradeoff between cooperation burden and global performance optimality. Within each cluster, the agents coordinate their inputs to maximize their collective performance, while considering the coupling effect with external subsystems as uncertainty. By using a tube-based approach, the overall system state is driven to the target sets while satisfying state and input constraints despite the changes in the controllers' clustering. Likewise, feasibility and stability of the closed-loop system are guaranteed by tracking techniques. The applicability of the proposed approach is illustrated by an academic example.
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2021.3133486