Cooperative distributed model predictive control based on topological hierarchy decomposition. (October 2020)

• Record Type: Journal Article
• Authors: Chen, Minghao; Zhao, Jun; Xu, Zuhua; Liu, Yuanlong; Zhu, Yucai; Shao, Zhijiang
• Is Part Of: Control engineering practice. Volume 103(2020)
• Publication Date: 2020-10
• Subjects: Distributed model predictive control -- Topological hierarchy decomposition -- Interpretive structural modeling; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2020.104578
• Languages: English
• ISSNs: 0967-0661

Abstract

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. Highlights: We present a cooperative DMPC based on topological hierarchy decomposition. Subsystems are decomposed into a hierarchy structure model. Less communication burden is needed to achieve a desired performance. Ensure the feasibility and stability properties of the proposed algorithm.