Abstract In this paper, a cooperative distributed model predictive control (DMPC) algorithm based on topological hierarchy decomposition is proposed. Utilizing the connection topology information of the distributed system, we decompose subsystems into a hierarchy structure model through interpretative structural modeling. Subsystems with strong coupling are grouped into the same layer, and weak coupling exists between subsystems in different layers. Then, we propose an improved cooperative DMPC algorithm, in which the optimal input trajectories of subsystems in each layer are evaluated and propagated in a hierarchical order. Instead of all-to-all communication, only intra-layer communication is required at each iteration of DMPC optimization, which can significantly lessen the communication burden without losing much performance. Furthermore, the feasibility and stability of the proposed algorithm are proven in detail. Finally, the effectiveness and merits of the proposed method are demonstrated by applying it to a reheating furnace system and a six-area power system.

中文翻译：

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基于拓扑层次分解的协同分布式模型预测控制

摘要 本文提出了一种基于拓扑层次分解的协同分布式模型预测控制(DMPC)算法。利用分布式系统的连接拓扑信息，通过解释性结构建模将子系统分解为层次结构模型。强耦合的子系统归入同一层，不同层的子系统之间存在弱耦合。然后，我们提出了一种改进的协作 DMPC 算法，其中每层子系统的最佳输入轨迹被评估并以分层顺序传播。DMPC优化的每一次迭代只需要层内通信，而不是all to all通信，这样可以在不损失太多性能的情况下显着减轻通信负担。此外，详细证明了所提算法的可行性和稳定性。最后，通过将其应用于加热炉系统和六区电力系统，证明了所提出方法的有效性和优点。