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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.

Relevant to DT products

Original headline
Observed Control - Linearly Scalable Nonlinear Model Predictive Control with Adaptive Horizons
Read the original: arXiv — Systems and Control (eess.SY)

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