A New Weighted Graph-Based Partitioning Algorithm for Decentralized Nonlinear Model Predictive Control of Large-Scale Systems

This paper proposes a grouping algorithm for partitioning large-scale nonlinear dynamical systems based on graph theory. The algorithm incorporates a novel scheme to quantify the strengths of graph edges, representing the degree of couplings among the system variables via sensitivity functions. This leads to a weighted graph topology with different weights on the obtained graph edges. An algorithm is then developed to partition systems into some sub-graphs based on the weighted graph. A decentralized nonlinear model predictive control (NMPC) methodology is then formulated for the sub-systems. The overall NMPC design methodology is finally evaluated on a process plant benchmark, consisting of two continuous stirred tank reactors (CSTRs) and a flash separator with a recycle path. A set of tracking and regulatory tests is comparatively conducted exploring the successful performance of the proposed algorithm in the context of the decentralized NMPC methodology with respect to an alternative centralized NMPC control scheme.