Design and Experimental Evaluation of Non-Cooperative Distributed MPC for a Thermally Coupled System

Abstract

Model Predictive Control (MPC) is widely applied to multivariable systems due to its capability to systematically handle constraints and interactions. However, centralized MPC (CMPC) can become computationally intensive for strongly coupled systems, limiting its real-time applicability. Distributed Model Predictive Control (DMPC) addresses this limitation by decomposing the overall system into smaller subsystems, each controlled by a local MPC. This paper presents the design and experimental evaluation of a non-cooperative DMPC framework for the Temperature Control Laboratory (TCL) system, a thermally coupled two-input two-output process with nonlinear dynamics. In the proposed approach, each subsystem independently solves its local optimization problem while incorporating interaction effects through measured or predicted variables. Both sequential and iterative coordination strategies are implemented to examine the trade-off between computational efficiency and control performance. Experimental results under regulatory and servo conditions demonstrate that DMPC achieves effective disturbance rejection and accurate setpoint tracking with smooth control action. Sequential DMPC shows improved computational efficiency with comparable performance, whereas iterative DMPC enhances coordination at the expense of increased computational effort. These results highlight the practical potential of DMPC as a scalable alternative to centralized control for real-time applications in interconnected multivariable systems.