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On Feasibility, Stability and Performance in Distributed Model Predictive Control
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DOI：10.1109/TAC.2013.2285779.png)
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In distributed model predictive control (DMPC), where a centralized optimization problemis solved in distributed fashion using dual decomposition, it is important to keep the number of iterations in the solution algorithm small. In this technical note, we present a stopping condition to such distributed solution algorithms that is based on a novel adaptive constraint tightening approach. The stopping condition guarantees feasibility of the optimization problem and stability and a prespecified performance of the closed-loop system.
Keyword:
Distributed model predictive control
feasibility
performance guarantee
stability
期刊
IF:
7
论文数:
1.3W
被引数:
6.7W
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