Data-driven distributed EMPC for economic optimization of interconnected systems: A Hankel matrix approach

This paper presents a novel non-iterative data-driven distributed economic model predictive control (EMPC) scheme for distributed linear time-invariant systems. The system consists of dynamically interacting subsystems that optimize an economic objective. Each subsystem solves its local optimization problem using input-output data. It also considers assumed neighbor trajectories subject to a consistency constraint derived from Hankel matrices. A data-driven framework is presented to achieve strong duality and dissipativity with a general supply rate. Additionally, a procedure is developed to design terminal ingredients for distributed EMPC using input-output trajectories. Theoretical analysis guarantees recursive feasibility and closed-loop stability. Simulation results on a building temperature control system demonstrate the energy efficiency and effectiveness of the proposed method compared with other MPC approaches.