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.
%0 Conference Paper
%1 ist:schlueter20a
%A Schlüter, H.
%A Allgöwer, F.
%B Proc. 21th IFAC World Congress
%C Berlin, Germany
%D 2020
%K grk2198 myown
%N 2
%P 7130-7135
%R 10.1016/j.ifacol.2020.12.518
%T A Constraint-Tightening Approach to Nonlinear Stochastic Model Predictive Control under General Bounded Disturbances
%U https://arxiv.org/abs/1912.01946
%V 53
%X 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.
@inproceedings{ist:schlueter20a,
abstract = {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.},
added-at = {2022-12-22T04:55:18.000+0100},
address = {Berlin, Germany},
author = {Schl{\"u}ter, H. and Allg{\"o}wer, F.},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/29d61ec417c8b41f5b06241d992fddce5/hschluter},
booktitle = {{P}roc. 21th {IFAC} {W}orld {C}ongress},
doi = {10.1016/j.ifacol.2020.12.518},
interhash = {6968d4f868f1bbb4616eec8cd1adb9a1},
intrahash = {9d61ec417c8b41f5b06241d992fddce5},
keywords = {grk2198 myown},
month = {7},
number = 2,
pages = {7130-7135},
preprinturl = {https://arxiv.org/abs/1912.01946},
timestamp = {2023-01-19T07:57:03.000+0100},
title = {A Constraint-Tightening Approach to Nonlinear Stochastic Model Predictive Control under General Bounded Disturbances},
url = {https://arxiv.org/abs/1912.01946},
volume = 53,
year = 2020
}