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  4. SVR-AMA: An Asynchronous Alternating Minimization Algorithm With Variance Reduction for Model Predictive Control Applications
 
research article

SVR-AMA: An Asynchronous Alternating Minimization Algorithm With Variance Reduction for Model Predictive Control Applications

Ferranti, Laura
•
Pu, Ye
•
Jones, Colin N.  
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May 1, 2019
Ieee Transactions On Automatic Control

This paper focuses on the design of an asynchronous dual solver suitable for 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 proximal stochastic gradient methods (Prox-SVRG) and on the alternating minimization algorithm (AMA). The resultant algorithm, a stochastic AMA with VR (SVR-AMA), 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 one to tune the probability of the asynchronous updates to improve the quality of the estimates. Two novel accelerated versions of the Prox-SVRG (and, by duality, of SVR-AMA) are also provided. We apply the proposed algorithm to a specific class of splitting methods, that is, the decomposition along the length of the prediction horizon. Numerical results on the longitudinal control problem of an Airbus passenger aircraft show the benefits that we can gain in terms of computation time when using our proposed solver with an adaptive probability distribution.

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Type
research article
DOI
10.1109/TAC.2018.284956
Web of Science ID

WOS:000466226500003

Author(s)
Ferranti, Laura
Pu, Ye
Jones, Colin N.  
Keviczky, Tamas
Date Issued

2019-05-01

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

Published in
Ieee Transactions On Automatic Control
Volume

64

Issue

5

Start page

1800

End page

1815

Subjects

Automation & Control Systems

•

Engineering, Electrical & Electronic

•

Automation & Control Systems

•

Engineering

•

aerospace

•

control systems

•

linear systems

•

optimization methods

•

predictive control

•

quadratic programming

•

gradient-method

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LA3  
Available on Infoscience
June 18, 2019
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/157619
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