Abstract
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 language | English |
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Title of host publication | Proceedings 2016 IEEE 55th Conference on Decision and Control (CDC) |
Editors | Francesco Bullo, Christophe Prieur, Alessandro Giua |
Place of Publication | Piscataway, NJ, USA |
Publisher | IEEE |
Pages | 2345-2350 |
ISBN (Print) | 978-1-5090-1837-6 |
DOIs | |
Publication status | Published - 2016 |
Event | 55th IEEE Conference on Decision and Control, CDC 2016 - Las Vegas, United States Duration: 12 Dec 2016 → 14 Dec 2016 |
Conference
Conference | 55th IEEE Conference on Decision and Control, CDC 2016 |
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Abbreviated title | CDC 2016 |
Country/Territory | United States |
City | Las Vegas |
Period | 12/12/16 → 14/12/16 |