Distributed Real-Time Cooperative Model Predictive Control

Overview & Synopsis

This paper presents a novel distributed real-time iterative method for cooperative linear Model Predictive Control (MPC) with polyhedral state and input constraints, implementing a relaxed, recentered logarithmic barrier function. The method transforms centralized MPC problems into a distributed framework using temporal-spatial splitting, facilitating efficient online implementation. To achieve real-time performance, we introduce a distributed convex nonlinear optimization solver with guaranteed global linear convergence. By executing this solver with a fixed number of iterations per sampling time, the resulting near-optimal MPC controller ensures closed-loop stability while maintaining fixed computational complexity. The proposed approach is validated through an illustrative example involving a spring-vehicle-damper system benchmark, demonstrating both its effectiveness and computational efficiency.