MPC on state space models with stochastic input map
This paper considers a state space model with a stochastic input map. The reference tracking problem is recast as a regulation problem involving both a stochastic input map and an additive term. First we demonstrate that, subject to a mean square stability condition on a feedback control law, the va...
| Main Authors: | , , , |
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| Format: | Conference item |
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2006
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| _version_ | 1797052826030964736 |
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| author | Couchman, P Kouvaritakis, B Cannon, M IEEE |
| author_facet | Couchman, P Kouvaritakis, B Cannon, M IEEE |
| author_sort | Couchman, P |
| collection | OXFORD |
| description | This paper considers a state space model with a stochastic input map. The reference tracking problem is recast as a regulation problem involving both a stochastic input map and an additive term. First we demonstrate that, subject to a mean square stability condition on a feedback control law, the variance of the state converges to a constant in prediction. A stage cost is then chosen as a weighted sum of the mean and the variance of the output of the state space model. An MPC controller based around quasi-closed loop predictions and a dual-mode prediction horizon is defined. This controller is shown to provide a form of stochastic convergence of the state to an ellipsoidal set. © 2006 IEEE. |
| first_indexed | 2024-03-06T18:36:07Z |
| format | Conference item |
| id | oxford-uuid:0b4a2d50-d5d4-4717-8396-88351b375919 |
| institution | University of Oxford |
| last_indexed | 2024-03-06T18:36:07Z |
| publishDate | 2006 |
| record_format | dspace |
| spelling | oxford-uuid:0b4a2d50-d5d4-4717-8396-88351b3759192022-03-26T09:28:33ZMPC on state space models with stochastic input mapConference itemhttp://purl.org/coar/resource_type/c_5794uuid:0b4a2d50-d5d4-4717-8396-88351b375919Symplectic Elements at Oxford2006Couchman, PKouvaritakis, BCannon, MIEEEThis paper considers a state space model with a stochastic input map. The reference tracking problem is recast as a regulation problem involving both a stochastic input map and an additive term. First we demonstrate that, subject to a mean square stability condition on a feedback control law, the variance of the state converges to a constant in prediction. A stage cost is then chosen as a weighted sum of the mean and the variance of the output of the state space model. An MPC controller based around quasi-closed loop predictions and a dual-mode prediction horizon is defined. This controller is shown to provide a form of stochastic convergence of the state to an ellipsoidal set. © 2006 IEEE. |
| spellingShingle | Couchman, P Kouvaritakis, B Cannon, M IEEE MPC on state space models with stochastic input map |
| title | MPC on state space models with stochastic input map |
| title_full | MPC on state space models with stochastic input map |
| title_fullStr | MPC on state space models with stochastic input map |
| title_full_unstemmed | MPC on state space models with stochastic input map |
| title_short | MPC on state space models with stochastic input map |
| title_sort | mpc on state space models with stochastic input map |
| work_keys_str_mv | AT couchmanp mpconstatespacemodelswithstochasticinputmap AT kouvaritakisb mpconstatespacemodelswithstochasticinputmap AT cannonm mpconstatespacemodelswithstochasticinputmap AT ieee mpconstatespacemodelswithstochasticinputmap |