Scenario-Based Probabilistic Reachable Sets for Recursively Feasible Stochastic Model Predictive Control

Open access
Date
2020-04Type
- Journal Article
Citations
Cited 19 times in
Web of Science
Cited 20 times in
Scopus
ETH Bibliography
yes
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Abstract
This letter presents a stochastic model predictive control approach (MPC) for linear discrete-time systems subject to unbounded and correlated additive disturbance sequences, which makes use of the scenario approach for offline computation of probabilistic reachable sets. These sets are used in a tube-based MPC formulation, resulting in low computational requirements. Using a recently proposed MPC initialization scheme and nonlinear tube controllers, we provide recursive feasibility and closed-loop chance constraint satisfaction, as well as hard input constraint guarantees, which are typically challenging in tube-based formulations with unbounded noise. The approach is demonstrated in simulation for the control of an overhead crane system. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000389523Publication status
publishedExternal links
Journal / series
IEEE Control Systems LettersVolume
Pages / Article No.
Publisher
IEEESubject
Predictive control for linear systems; Constrained control; Stochastic optimal controlOrganisational unit
09563 - Zeilinger, Melanie / Zeilinger, Melanie
Funding
157601 - Safety and Performance for Human in the Loop Control (SNF)
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Show all metadata
Citations
Cited 19 times in
Web of Science
Cited 20 times in
Scopus
ETH Bibliography
yes
Altmetrics