Trading performance for state constraint feasibility in stochastic constrained control: a randomized approach
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Publication:509522
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
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
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