Distributed model predictive control of dynamically decoupled systems with coupled cost

This paper considers the distributed model predictive control (DMPC) of systems with interacting subsystems having decoupled dynamics and constraints but coupled costs. An easily-verifiable constraint is introduced to ensure asymptotic stability of the overall system in the absence of disturbance. T...

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Bibliographic Details
Published in:Automatica (Oxford) 2010-12, Vol.46 (12), p.2053-2058
Main Authors: Wang, Chen, Ong, Chong-Jin
Format: Article
Language:English
Subjects:
Applied sciences
Asymptotic properties
Computer science
control theory
systems
Control system analysis
Control systems
Control theory. Systems
Distributed control
Disturbances
Dynamical systems
Dynamics
Exact sciences and technology
Invariants
Mathematical models
Model predictive control
Optimal control
Predictive control
Stability
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Summary:This paper considers the distributed model predictive control (DMPC) of systems with interacting subsystems having decoupled dynamics and constraints but coupled costs. An easily-verifiable constraint is introduced to ensure asymptotic stability of the overall system in the absence of disturbance. The constraint introduced has a parameter which allows for the performance of the DMPC system to approach that controlled by a centralized model predictive controller. When the subsystems are linear and additive disturbance is present, the added constraint ensures the state of each subsystem converges to its respective minimal disturbance invariant set. The approach is demonstrated via several numerical examples.
ISSN:0005-1098
1873-2836
DOI:10.1016/j.automatica.2010.09.002