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Linearly Scalable Nonlinear MPC with Adaptive Horizons
US & West ·
Observed control leverages the duality between state estimation and model predictive control to compute control actions with linear scalability in prediction horizon length. The algorithms provide adaptive horizon lengths and early termination criteria, using Kalman smoothers as the backend. A separate formulation splits linear MPC into purely reactive and anticipatory components.
Why it matters
Linear horizon scalability and any-time operation are important for real-time management of large-scale systems.
arXiv:2602.01164v2 Announce Type: replace-cross Abstract: We propose a computationally tractable, tube-based robust nonlinear model predictive control (MPC) framework using difference-of-convex (DC) functions and sequential convex programming.
arXiv:2609.38552v1 Announce Type: cross Abstract: Public institutions increasingly procure AI systems whose design they cannot inspect or change. In higher education, proprietary Early Warning Systems (EWS) leave colleges with few options beyond adjusting model outputs to address inequity.
arXiv:2609.29100v2 Announce Type: replace Abstract: This paper investigates model predictive control (MPC) for switched systems subject to control and state constraints. A variable-horizon switched MPC approach is proposed.
