A Constraint-Tightening Approach to Nonlinear Stochastic Model Predictive Control under General Bounded Disturbances⁎⁎The authors thank the German Research Foundation (DFG) for financial support under the Grant AL 316/12-2-279734922; and the International Max Planck Research School for Intelligent Systems (IMPRS-IS) for supporting Henning Schlüter

This paper presents a nonlinear model predictive control strategy for stochastic systems with state- and input-dependent, finite-support disturbances subject to individual chance constraints. Our approach uses an online computed stochastic tube to ensure stability, constraint satisfaction, and recur...

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Bibliographic Details
Published inIFAC-PapersOnLine Vol. 53; no. 2; pp. 7130 - 7135
Main Authors Schlüter, Henning, Allgöwer, Frank
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 2020
Subjects
Constrained control
Nonlinear control
Predictive control
Stochastic control
Constrained control
Stochastic control
Nonlinear control
Predictive control
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ISSN2405-8963
2405-8963
DOI10.1016/j.ifacol.2020.12.518

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Summary:This paper presents a nonlinear model predictive control strategy for stochastic systems with state- and input-dependent, finite-support disturbances subject to individual chance constraints. Our approach uses an online computed stochastic tube to ensure stability, constraint satisfaction, and recursive feasibility in the presence of stochastic uncertainties. The shape of the tube and the constraint backoff is based on an offline computed incremental Lyapunov function.
ISSN:2405-8963
2405-8963
DOI:10.1016/j.ifacol.2020.12.518