Trading performance for state constraint feasibility in stochastic constrained control: a randomized approach

DOI10.1016/j.jfranklin.2016.10.019zbMath1355.93172OpenAlexW2538760422MaRDI QIDQ509522

Simone Garatti, Luca Deori, Maria Prandini

Publication date: 9 February 2017

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

Full work available at URL: http://hdl.handle.net/11311/1009687

zbMATH Keywords

constrained controlfinite control horizonstochastic linear systems

Mathematics Subject Classification ID

Design techniques (robust design, computer-aided design, etc.) (93B51) Linear systems in control theory (93C05) Stochastic systems in control theory (general) (93E03)

Related Items (5)

Probabilistic performance validation of deep learning‐based robust NMPC controllers ⋮ Constrained tracking control of stochastic multivariable nonlinear systems via Gaussian process predictions ⋮ Full probabilistic solution of a finite dimensional linear control system with random initial and final conditions ⋮ Trading performance for state constraint feasibility in stochastic constrained control: a randomized approach ⋮ A randomized relaxation method to ensure feasibility in stochastic control of linear systems subject to state and input constraints

Uses Software

• CVX
• YALMIP

Cites Work

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