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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:2509.05463v3 Announce Type: replace Abstract: This paper proposes a novel approach to design analog electronic circuits that implement Model Predictive Control (MPC) policies for dynamical systems described by affine models.
arXiv:2604.01730v3 Announce Type: replace-cross Abstract: This paper investigates Koopman operator-based approaches for multivariable control of a two-spool turbofan engine. A physics-based component-level model is developed to generate training data and validate the controllers.
A ruleless digital twin approach is proposed where decisions are generated automatically from purely declarative user specifications instead of hand-written rules. This reduces the complexity of building and maintaining twins under dynamically changing conditions such as weather or dynamic energy pricing.
