Introduction to the special issue on hierarchical and distributed model predictive control
1. Overview
The origins of this special issue of the Journal of Process Control (JPC) on "Hierarchical and Distributed Model Predictive Control" lie in the European 7th framework STREP project "Hierarchical and distributed model predictive control of large-scale complex systems (HD-MPC)", which aims at developing new and efficient methods for distributed and hierarchical control of large-scale, complex, networked systems with many embedded sensors and actuators, and characterized by complex dynamics and mutual influences.
For this special issue we invited 20 teams participating in the HD-MPC project as well in other related European projects such as WIDE ("Decentralized and Wireless Control of Large-Scale Systems"), EMBOCON ("Embedded Optimization for Resource Constrained Platforms"), and HYCON2 ("Highly-complex and networked control systems") to submit a contribution. This resulted in 14 papers being submitted. These papers were next subjected to the regular JPC review process. In this context it is important to mention that the reviews of the papers co-authored by one of the guest editors were managed independently by JPC's regional editor for Europe, Prof. Dochain. Moreover, in order to guarantee an independent and anonymous review process, the papers co-authored by the Editor-in-Chief of JPC, Prof. Marquardt, were partly 1 handled outside the JPC on-line review system. The overall review process resulted in 12 papers being accepted, after one or more revisions. These papers are all included in this special issue.
The special issue covers four main subareas of the broad field of hierarchical and distributed Model Predictive Control (MPC), where some papers belong to more than one category: distributed control, distributed optimization, distributed estimation, and applications. More specifically, the papers on distributed control cover negotiation and coordination methods, decentralized control, and hierarchical control. The papers on distributed optimization include decomposition methods, partitioning methods, and parallel algorithms. The applications considered involve chemical processes, temperature control, hydro-power plants, irrigation and water distribution networks, and a real-life quadruple-tank process.