A multirate variational approach to nonlinear MPC
A multirate nonlinear model predictive control (NMPC) strategy is proposed for systems with dynamics and control inputs evolving on different timescales. The proposed multirate formulation of the system model and receding horizon optimal control problem allows larger time steps in the prediction hor...
| Main Authors: | , , |
|---|---|
| Format: | Conference item |
| Language: | English |
| Published: |
IEEE
2022
|
| _version_ | 1826308277560934400 |
|---|---|
| author | Lishkova, Y Cannon, MR Ober-Blobaum, S |
| author_facet | Lishkova, Y Cannon, MR Ober-Blobaum, S |
| author_sort | Lishkova, Y |
| collection | OXFORD |
| description | A multirate nonlinear model predictive control
(NMPC) strategy is proposed for systems with dynamics and
control inputs evolving on different timescales. The proposed
multirate formulation of the system model and receding horizon optimal control problem allows larger time steps in the
prediction horizon compared to single-rate schemes, providing
computational savings while ensuring recursive feasibility. A
multirate variational model is used with a tube-based successive linearization NMPC strategy. This allows either Jacobian
linearization or linearization using quadratic and linear Taylor series approximations of the Lagrangian and generalized
forces respectively, providing alternative means for computing
linearization error bounds. The two approaches are shown to
be equivalent for a specific choice of approximation points and
their structure-preserving properties are investigated. Numerical examples are provided to illustrate the multirate approach,
its conservation properties and computational savings. |
| first_indexed | 2024-03-07T07:17:10Z |
| format | Conference item |
| id | oxford-uuid:a574e785-9098-4403-988f-68d419effad3 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:17:10Z |
| publishDate | 2022 |
| publisher | IEEE |
| record_format | dspace |
| spelling | oxford-uuid:a574e785-9098-4403-988f-68d419effad32022-08-15T11:45:38ZA multirate variational approach to nonlinear MPCConference itemhttp://purl.org/coar/resource_type/c_5794uuid:a574e785-9098-4403-988f-68d419effad3EnglishSymplectic ElementsIEEE2022Lishkova, YCannon, MROber-Blobaum, SA multirate nonlinear model predictive control (NMPC) strategy is proposed for systems with dynamics and control inputs evolving on different timescales. The proposed multirate formulation of the system model and receding horizon optimal control problem allows larger time steps in the prediction horizon compared to single-rate schemes, providing computational savings while ensuring recursive feasibility. A multirate variational model is used with a tube-based successive linearization NMPC strategy. This allows either Jacobian linearization or linearization using quadratic and linear Taylor series approximations of the Lagrangian and generalized forces respectively, providing alternative means for computing linearization error bounds. The two approaches are shown to be equivalent for a specific choice of approximation points and their structure-preserving properties are investigated. Numerical examples are provided to illustrate the multirate approach, its conservation properties and computational savings. |
| spellingShingle | Lishkova, Y Cannon, MR Ober-Blobaum, S A multirate variational approach to nonlinear MPC |
| title | A multirate variational approach to nonlinear MPC |
| title_full | A multirate variational approach to nonlinear MPC |
| title_fullStr | A multirate variational approach to nonlinear MPC |
| title_full_unstemmed | A multirate variational approach to nonlinear MPC |
| title_short | A multirate variational approach to nonlinear MPC |
| title_sort | multirate variational approach to nonlinear mpc |
| work_keys_str_mv | AT lishkovay amultiratevariationalapproachtononlinearmpc AT cannonmr amultiratevariationalapproachtononlinearmpc AT oberblobaums amultiratevariationalapproachtononlinearmpc AT lishkovay multiratevariationalapproachtononlinearmpc AT cannonmr multiratevariationalapproachtononlinearmpc AT oberblobaums multiratevariationalapproachtononlinearmpc |