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Distributed Model Predictive Control for Reconfigurable Systems With Network Connection
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DOI：10.1109/TASE.2021.3058298.png)
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Abstract
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This article proposes a distributed model predictive control (DMPC) strategy for a class of large-scale systems composed of several interacting subsystems. When a certain subsystem is required to be removed or inserted, the topology change of the system network can lead to the infeasibility of interacting local controllers due to the existence of the interactions among subsystems. In this article, the interactions among subsystems are presented as state trajectory estimations of interacting subsystems, and the estimations are involved in each local MPC. To deal with the influence resulted from the change of system topology, optimization schemes for removal and plugging-in are designed and employed in the proposed strategy. They optimize related subsystems' reference trajectories, which are used to approximate the interacting state trajectories here, to reduce the time it takes to drive the system states and reference trajectories to a region. This region ensures that the system topology change can be conducted with all controllers having feasible solutions. The proposed DMPC algorithm has the following characteristics: 1) all the optimization problems in each MPC are solved in a noniterative manner and each controller only communicates with its neighbors and 2) it guarantees the feasibility of all controllers throughout the topology change process and the convergence of the system after the topology change. Simulation results show the effectiveness of the proposed DMPC algorithm.
Keywords:
Topology
Optimization
Network topology
Trajectory
Control systems
Switches
Couplings
Distributed model predictive control (DMPC)
large-scale systems
model predictive control (MPC)
reconfiguration
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