Conference item
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 real time. To demonstrate the properties of the algorithm we conduct numerical experiments on randomly generated problems, and show that the algorithm is effective for achieving an approximate solution, but has limitations when an exact solution is required.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
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(Preview, Accepted manuscript, pdf, 431.5KB, Terms of use)
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- Publisher copy:
- 10.23919/ACC.2018.8431655
Authors
- Publisher:
- Institute of Electrical and Electronics Engineers
- Host title:
- 2018 American Control Conference (ACC 2018)
- Journal:
- American Control Conference (ACC 2018) More from this journal
- Publication date:
- 2018-08-16
- Acceptance date:
- 2018-01-23
- DOI:
- Pubs id:
-
pubs:854358
- UUID:
-
uuid:46096d3c-de47-4d84-aa14-1be74d0403a6
- Local pid:
-
pubs:854358
- Source identifiers:
-
854358
- Deposit date:
-
2018-05-31
Terms of use
- Copyright holder:
- American Automatic Control Council
- Copyright date:
- 2018
- Notes:
- ©2018 AACC
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