Improving the Performance of Distributed Model Predictive Control by Applying Graph Partitioning Methods

The major part of the execution time of distributed algorithms is required for the communication between agents. This paper approaches a reduction of the communication effort by reducing the number of edges in the considered graph. This is achieved by partitioning the graph and formulating a super graph. At first, the computational and communication effort is evaluated on an abstract level independent of the distributed algorithm, before the Alternating Direction Method of Multipliers (ADMM) is applied to a system of coupled water tanks. This allows to outline the trade-off between computation and communication time and to evaluate an optimal number of partitions that minimizes the execution time. The influence of the partitioning on the convergence behavior of the distributed algorithm is studied and compared with the concept of neighbor approximation.