Partitioning for large-scale systems: A sequential distributed MPC design

Conference Article


IFAC World Congress (IFAC)





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Large-scale systems involve a high number of variables making challenging the design of controllers because of information availability and computational burden issues. Normally, the measurement of all the states in a large-scale system implies to have a big communication network, which might be quite expensive. On the other hand, the treatment of large amount of data to compute the appropriate control inputs implies high computational costs. An alternative to mitigate the aforementioned issues is to split the problem into several sub-systems. Thus, computational tasks may be split and assigned to dierent local controllers, letting to reduce the required time to compute the control inputs. Additionally, the partitioning of the system allows control designers to simplify the communication network. This paper presents a partitioning algorithm performed by considering an information-sharing graph that can be generated for any control strategy and for any dynamical large-scale system. Finally, a distributed model predictive control (DMPC) is designed for a large-scale system as an application of the proposed partitioning method.


optimal control, predictive control.

Author keywords

distributed control, system partitioning

Scientific reference

J. Barreiro-Gomez, C. Ocampo-Martínez and N. Quijano. Partitioning for large-scale systems: A sequential distributed MPC design, 20th IFAC World Congress, 2017, Toulouse, France, Vol 50 of IFAC Papers Online, pp. 8838-8843, Elsevier.