Reducing variables in model predictive control of linear system with disturbances using singular value decomposition

DOI10.1016/j.sysconle.2014.06.002zbMath1296.93060OpenAlexW2027763781MaRDI QIDQ460602

J. Herrera, D. Rodríguez-Gómez

Publication date: 13 October 2014

Published in: Systems \& Control Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.sysconle.2014.06.002

zbMATH Keywords

domain of attractionmodel predictive controlreduced number of variables

Mathematics Subject Classification ID

Related Items (6)

Output feedback robust MPC for LPV system with polytopic model parametric uncertainty and bounded disturbance ⋮ Optimal move blocking strategies for model predictive control ⋮ Orthonormal function parametrisation of model-predictive control for linear time-varying systems ⋮ Distributed MPC of constrained linear systems with time-varying terminal sets ⋮ Move blocked model predictive control with guaranteed stability and improved optimality using linear interpolation of base sequences ⋮ NMPC in active subspaces: dimensionality reduction with recursive feasibility guarantees

Cites Work

• Robust variable horizon MPC with move blocking
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• Convergence properties of constrained linear system under MPC control law using affine disturbance feedback
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• Generalized predictive control. I: The basic algorithm
• Theory and computation of disturbance invariant sets for discrete-time linear systems
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• Robust model predictive control of constrained linear systems with bounded disturbances
• The minimal disturbance invariant set: outer approximations via its partial sums
• Control of constrained dynamic systems
• Reduced order model predictive control for constrained discrete-time linear systems
• An SVD based strategy for receding horizon control of input constrained linear systems
• Invariant approximations of the minimal robust positively Invariant set
• Systems with persistent disturbances: Predictive control with restricted constraints
• Unnamed Item

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