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.
Linear horizon scalability and any-time operation are important for real-time management of large-scale systems.
Translation and summary are machine-generated from the source. The full article is not republished; its rights belong to the source.
