ADMM for MPC with state and input constraints, and input nonlinearity
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 o...
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| Format: | Conference item |
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Institute of Electrical and Electronics Engineers
2018
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| _version_ | 1826270080472711168 |
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| author | East, S Cannon, M |
| author_facet | East, S Cannon, M |
| author_sort | East, S |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-06T21:35:15Z |
| format | Conference item |
| id | oxford-uuid:46096d3c-de47-4d84-aa14-1be74d0403a6 |
| institution | University of Oxford |
| last_indexed | 2024-03-06T21:35:15Z |
| publishDate | 2018 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | oxford-uuid:46096d3c-de47-4d84-aa14-1be74d0403a62022-03-26T15:11:22ZADMM for MPC with state and input constraints, and input nonlinearityConference itemhttp://purl.org/coar/resource_type/c_5794uuid:46096d3c-de47-4d84-aa14-1be74d0403a6Symplectic Elements at OxfordInstitute of Electrical and Electronics Engineers2018East, SCannon, MIn 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. |
| spellingShingle | East, S Cannon, M ADMM for MPC with state and input constraints, and input nonlinearity |
| title | ADMM for MPC with state and input constraints, and input nonlinearity |
| title_full | ADMM for MPC with state and input constraints, and input nonlinearity |
| title_fullStr | ADMM for MPC with state and input constraints, and input nonlinearity |
| title_full_unstemmed | ADMM for MPC with state and input constraints, and input nonlinearity |
| title_short | ADMM for MPC with state and input constraints, and input nonlinearity |
| title_sort | admm for mpc with state and input constraints and input nonlinearity |
| work_keys_str_mv | AT easts admmformpcwithstateandinputconstraintsandinputnonlinearity AT cannonm admmformpcwithstateandinputconstraintsandinputnonlinearity |