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ADMM for MPC with state and input constraints, and input nonlinearity

Abstract:

In this paper we propose an Alternating Direction Method of Multipliers (ADMM) algorithm for solving a Model Predictive Control (MPC) optimization problem, in which the system has state and input constraints and a nonlinear input map. The resulting optimization is nonconvex, and we provide a proof of convergence to a point satisfying necessary conditions for optimality. This general method is proposed as a solution for blended mode control of hybrid electric vehicles, to allow optimization in...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.23919/ACC.2018.8431655

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
St John's College
Role:
Author
Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Journal:
American Control Conference (ACC 2018) Journal website
Host title:
2018 American Control Conference (ACC 2018)
Publication date:
2018-08-16
Acceptance date:
2018-01-23
DOI:
Source identifiers:
854358
Pubs id:
pubs:854358
UUID:
uuid:46096d3c-de47-4d84-aa14-1be74d0403a6
Local pid:
pubs:854358
Deposit date:
2018-05-31

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