http://bme2.aut.ac.ir/~towhidkhah/MPC/seminars-ppt/seminar%20MPC/Fazane%20karami/Reference/Architectures%20for%20distributed%20and%20hierarchical%20Model%20Predictive%20Control%20%e2%80%93%20A%20review.pdf

Architectures for distributed and hierarchical Model Predictive Control - A review

A distributed model predictive control (MPC) framework, suitable for controlling large-scale networked systems such as power systems, is presented. The overall system is decomposed into subsystems, each with its own MPC controller. These subsystem-based MPCs work iteratively and cooperatively towards satisfying systemwide control objectives. If available computational time allows convergence, the proposed distributed MPC framework achieves performance equivalent to centralized MPC. Furthermore, the distributed MPC algorithm is feasible and closed-loop stable under intermediate termination. Automatic generation control (AGC) provides a practical example for illustrating the efficacy of the proposed distributed MPC framework.

Distributed MPC Strategies With Application to Power System Automatic Generation Control

http://pdclab.seas.ucla.edu/Publications/PDChristofides/PDChristofides_RScattolini_DMunozdelaPena_JLiu_CCE_2013_21_DMPC_A_Tutorial_Review_and_Future_Research_Directions.pdf

Distributed model predictive control: A tutorial review and future research directions

https://escholarship.org/content/qt3hw060jt/qt3hw060jt.pdf?t=lnpyzn

Distributed receding horizon control for multi-vehicle formation stabilization

The objective of this paper is to provide a review of distributed control and management strategies for the next generation power system in the context of microgrids. This paper also identifies future research directions. The next generation power system, also referred to as the smart grid, is distinct from the existing power system due to its extensive use of integrated communication, advanced components such as power electronics, sensing, and measurement, and advanced control technologies. At the same time, the need for increased number of small distributed and renewable energy resources can exceed the capabilities of an available computational resource. Therefore, the recent literature has seen a significant research effort on dividing the control task among different units, which gives rise to the development of several distributed techniques. This paper discusses features and characteristics of these techniques, and identifies challenges and opportunities ahead. The paper also discusses the relationship between distributed control and hierarchical control.

Distributed Control Techniques in Microgrids

The article presents results for distributed model predictive control (MPC), focusing on i) the coordination of the optimization computations using iterative exchange of information and ii) the stability of the closed-loop system when information is exchanged only after each iteration. Current research is focusing on general methods for decomposing large-scale problems for distributed MPC and methods for guaranteeing stability when multiple agents are controlling systems subject to abrupt changes.

D. Jia

Papers

Distributed model predictive control