Distributed model predictive control for linear systems under communication noise: Algorithm, theory and implementation

We study the distributed model predictive control (DMPC) problem for a network of linear discrete-time subsystems in the presence of stochastic noise among communication channels, where the system dynamics are decoupled and the system constraints are coupled. The DMPC is cast as a stochastic distrib...

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Veröffentlicht in:Automatica (Oxford) Jg. 125; S. 109422
Hauptverfasser: Li, Huiping, Jin, Bo, Yan, Weisheng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.03.2021
Schlagworte:
Communication noise
Distributed model predictive control (DMPC)
Global constraints
Stochastic alternating direction multiplier method (ADMM)
Stochastic alternating direction multiplier method (ADMM)
Communication noise
Global constraints
Distributed model predictive control (DMPC)
ISSN:0005-1098, 1873-2836
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Zusammenfassung:We study the distributed model predictive control (DMPC) problem for a network of linear discrete-time subsystems in the presence of stochastic noise among communication channels, where the system dynamics are decoupled and the system constraints are coupled. The DMPC is cast as a stochastic distributed consensus optimization problem by modeling the exchanged variables as stochastic ones and a novel noisy alternating direction multiplier method (NADMM) is proposed to solve it in a fully distributed way. We prove that the sequences of the primal and dual variables converge to their optimal values almost surely (a.s.) with communication noise. Furthermore, a new stopping criterion and a DMPC scheme termed as current–previous DMPC (cpDMPC) are proposed, which guarantees deterministic termination even when the NADMM algorithm may not converge in a practical realization. Next, the strict analysis on the feasibility of the cpDMPC strategy and the closed-loop stability is carried out, and it is shown that the cpDMPC strategy is feasible at each time step and the closed-loop system is asymptotically stable. Finally, the effectiveness of the proposed NADMM algorithm is verified via an example.
ISSN:0005-1098
1873-2836
DOI:10.1016/j.automatica.2020.109422