Data-driven subspace predictive control: stability and horizon tuning

DOI10.1016/J.JFRANKLIN.2018.07.032zbMath1398.93279OpenAlexW2888382725WikidataQ129342683 ScholiaQ129342683MaRDI QIDQ1797194

Saba Sedghizadeh, Soosan Beheshti

Publication date: 18 October 2018

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2018.07.032

zbMATH Keywords

robustnessclosed-loop stabilitysubspace predictive control

Mathematics Subject Classification ID

Sensitivity (robustness) (93B35) Multivariable systems, multidimensional control systems (93C35) Lyapunov and other classical stabilities (Lagrange, Poisson, (L^p, l^p), etc.) in control theory (93D05) Software, source code, etc. for problems pertaining to systems and control theory (93-04)

Cites Work

• Unnamed Item
• Robustly asymptotically stable finite-horizon MPC
• On the choice of the horizon in long-range predictive control - some simple criteria
• On type 1 systems and the Clarke-Gawthrop regulator
• Identification of the deterministic part of MIMO state space models given in innovations form from input-output data
• Adaptive predictive control: a data-driven closed-loop subspace identification approach
• Constrained model predictive control: Stability and optimality
• Stable predictive control horizons
• Infinite horizon stable predictive control

This page was built for publication: Data-driven subspace predictive control: stability and horizon tuning