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Distributed Real-Time MPC with Convergence Guarantees
US & West ·
A distributed iterative method is presented for cooperative linear MPC with polyhedral state and input constraints, using a logarithmic barrier function. The centralized problem is turned into a distributed scheme with a convex nonlinear solver guaranteeing global linear convergence. A fixed iteration count per sampling step ensures closed-loop stability at constant computational cost.
Why it matters
Real-time distributed solvers matter for controlling systems that cannot be handled by a single centralized optimizer.
Original headline
Distributed Real-Time Cooperative Model Predictive Control
arXiv:2610.29559v1 Announce Type: new Abstract: Model predictive control is a high-performance control method, but it incurs comparatively high online computational costs across many control tasks and is challenging to realize in low-level hardware settings.
arXiv:2610.29377v1 Announce Type: cross Abstract: Learning-based path planners based on diffusion and flow matching can generate diverse trajectories from demonstrations but typically lack mechanisms for constraint satisfaction and collision avoidance during deployment.
arXiv:2604.04455v3 Announce Type: replace Abstract: Nonlinear underactuated systems such as two-wheeled inverted pendulums (TWIPs) exhibit a limited region of attraction (RoA), which defines the set of initial conditions from which the closed-loop system converges to the equilibrium.
