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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:2610.36441v1 Announce Type: new Abstract: Periodic power fluctuations from artificial intelligence (AI) training workloads can excite oscillations in power systems. This paper proposes a model predictive control (MPC) framework that exploits short start-time delays to mitigate power…
arXiv:2207.10216v4 Announce Type: replace-cross Abstract: To address feasibility issues in model predictive control (MPC), most implementations relax state constraints by using slack variables and adding a penalty to the cost.
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.
