Asynchronous splitting design for Model Predictive Control

L. Ferranti, Y Pu, C.N. Jones, T. Keviczky

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)


This paper focuses on the design of an asynchronous dual solver suitable for embedded model predictive control (MPC) applications. The proposed solver relies on a state-of-the-art variance reduction (VR) scheme, previously used in the context of stochastic proximal gradient methods, and on the alternating minimization algorithm (AMA). The resultant algorithm, a stochastic AMA with VR, shows geometric convergence (in the expectation) to a suboptimal solution of the MPC problem and, compared to other state-of-the-art dual asynchronous algorithms, allows to tune the probability of the asynchronous updates to improve the quality of the estimates. We apply the proposed algorithm to a specific class of splitting methods, i.e., the decomposition along the length of the prediction horizon, and provide preliminary numerical results on a practical application, the longitudinal control of an Airbus passenger aircraft.

Original languageEnglish
Title of host publicationProceedings 2016 IEEE 55th Conference on Decision and Control (CDC)
EditorsFrancesco Bullo, Christophe Prieur, Alessandro Giua
Place of PublicationPiscataway, NJ, USA
ISBN (Print)978-1-5090-1837-6
Publication statusPublished - 2016
Event55th IEEE Conference on Decision and Control, CDC 2016 - Las Vegas, United States
Duration: 12 Dec 201614 Dec 2016


Conference55th IEEE Conference on Decision and Control, CDC 2016
Abbreviated titleCDC 2016
Country/TerritoryUnited States
CityLas Vegas


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