Distributed Model Predictive Control with suboptimality and stability guarantees

Theory for Distributed Model Predictive Control (DMPC) is developed based on dual decomposition of the convex optimization problem that is solved in each time sample. The process to be controlled is an interconnection of several subsystems, where each subsystem corresponds to a node in a graph. We p...

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Bibliographic Details
Published in49th IEEE Conference on Decision and Control (CDC) pp. 7272 - 7277
Main Authors Giselsson, P, Rantzer, A
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2010
Subjects
Asymptotic stability
Dynamic programming
Optimization
Predictive models
Stability criteria
Upper bound
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Summary:Theory for Distributed Model Predictive Control (DMPC) is developed based on dual decomposition of the convex optimization problem that is solved in each time sample. The process to be controlled is an interconnection of several subsystems, where each subsystem corresponds to a node in a graph. We present a stopping criterion for the DMPC scheme that can be locally verified by each node and that guarantees closed loop suboptimality above a pre-specified level and asymptotic stability of the interconnected system.
ISBN:142447745X
9781424477456
ISSN:0191-2216
DOI:10.1109/CDC.2010.5717026