A data-driven robust optimization approach to scenario-based stochastic model predictive control

•A novel data-driven approach is proposed for stochastic model predictive control.•Support vector clustering is adopted to learn an uncertainty set from scenarios.•A calibration procedure is developed to provide desirable probabilistic guarantees.•Finally, an uncertainty set-induced robust optimizat...

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Veröffentlicht in:	Journal of process control Jg. 75; S. 24 - 39
Hauptverfasser:	Shang, Chao, You, Fengqi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.03.2019
Schlagworte:	Chance constraints Machine learning Robust model predictive control Scenario programs Stochastic model predictive control Chance constraints Stochastic model predictive control Robust model predictive control Scenario programs Machine learning
ISSN:	0959-1524, 1873-2771
Online-Zugang:	Volltext
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