Distributed MPC for Reconfigurable Architecture Systems via Alternating Direction Method of Multipliers

This paper investigates the distributed model predictive control (MPC) problem of linear systems where the network topology is changeable by the way of inserting new subsystems, disconnecting existing subsystems, or merely modifying the couplings between different subsystems. To equip live systems w...

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Veröffentlicht in:IEEE/CAA journal of automatica sinica Jg. 8; H. 7; S. 1336 - 1344
Hauptverfasser: Bai, Ting, Li, Shaoyuan, Zou, Yuanyuan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 01.07.2021
Department of Automation,Shanghai Jiao Tong University,and Key Laboratory of System Control and Information Processing,Ministry of Education of China,Shanghai 200240,China
Schlagworte:
Algorithms
Alternating direction method of multipliers (ADMM) algorithm
Control systems
Convex functions
Couplings
distributed control
Iterative methods
Linear systems
model predictive control (MPC)
Multipliers
Network topologies
Optimal control
Optimization
Predictive control
Realignment
reconfigurable architecture systems
Reconfigurable architectures
Reconfiguration
Silicon
Subsystems
Systems architecture
reconfigurable architecture systems
distributed control
Alternating direction method of multipliers (ADMM) algorithm
model predictive control (MPC)
ISSN:2329-9266, 2329-9274
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Zusammenfassung:This paper investigates the distributed model predictive control (MPC) problem of linear systems where the network topology is changeable by the way of inserting new subsystems, disconnecting existing subsystems, or merely modifying the couplings between different subsystems. To equip live systems with a quick response ability when modifying network topology, while keeping a satisfactory dynamic performance, a novel reconfiguration control scheme based on the alternating direction method of multipliers (ADMM) is presented. In this scheme, the local controllers directly influenced by the structure realignment are redesigned in the reconfiguration control. Meanwhile, by employing the powerful ADMM algorithm, the iterative formulas for solving the reconfigured optimization problem are obtained, which significantly accelerate the computation speed and ensure a timely output of the reconfigured optimal control response. Ultimately, the presented reconfiguration scheme is applied to the level control of a benchmark four-tank plant to illustrate its effectiveness and main characteristics.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2329-9266
2329-9274
DOI:10.1109/JAS.2020.1003195