Stochastic tube MPC with state estimation
An output feedback Model Predictive Control (MPC) strategy for linear systems with additive stochastic disturbances and probabilistic constraints is proposed. Given the probability distributions of the disturbance input, the measurement noise and the initial state estimation error, the distributions...
| Main Authors: | , , , |
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| Format: | Journal article |
| Language: | English |
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2012
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| _version_ | 1797056151151443968 |
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| author | Cannon, M Cheng, Q Kouvaritakis, B Rakovic, S |
| author_facet | Cannon, M Cheng, Q Kouvaritakis, B Rakovic, S |
| author_sort | Cannon, M |
| collection | OXFORD |
| description | An output feedback Model Predictive Control (MPC) strategy for linear systems with additive stochastic disturbances and probabilistic constraints is proposed. Given the probability distributions of the disturbance input, the measurement noise and the initial state estimation error, the distributions of future realizations of the constrained variables are predicted using the dynamics of the plant and a linear state estimator. From these distributions, a set of deterministic constraints is computed for the predictions of a nominal model. The constraints are incorporated in a receding horizon optimization of an expected quadratic cost, which is formulated as a quadratic program. The constraints are constructed so as to provide a guarantee of recursive feasibility, and the closed loop system is stable in a mean-square sense. All uncertainties in this paper are taken to be boundedin most control applications this gives a more realistic representation of process and measurement noise than the more traditional Gaussian assumption. © 2012 Elsevier Ltd. All rights reserved. |
| first_indexed | 2024-03-06T19:19:20Z |
| format | Journal article |
| id | oxford-uuid:1986e9c6-2b15-411e-b5c3-c2521741b233 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T19:19:20Z |
| publishDate | 2012 |
| record_format | dspace |
| spelling | oxford-uuid:1986e9c6-2b15-411e-b5c3-c2521741b2332022-03-26T10:49:26ZStochastic tube MPC with state estimationJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:1986e9c6-2b15-411e-b5c3-c2521741b233EnglishSymplectic Elements at Oxford2012Cannon, MCheng, QKouvaritakis, BRakovic, SAn output feedback Model Predictive Control (MPC) strategy for linear systems with additive stochastic disturbances and probabilistic constraints is proposed. Given the probability distributions of the disturbance input, the measurement noise and the initial state estimation error, the distributions of future realizations of the constrained variables are predicted using the dynamics of the plant and a linear state estimator. From these distributions, a set of deterministic constraints is computed for the predictions of a nominal model. The constraints are incorporated in a receding horizon optimization of an expected quadratic cost, which is formulated as a quadratic program. The constraints are constructed so as to provide a guarantee of recursive feasibility, and the closed loop system is stable in a mean-square sense. All uncertainties in this paper are taken to be boundedin most control applications this gives a more realistic representation of process and measurement noise than the more traditional Gaussian assumption. © 2012 Elsevier Ltd. All rights reserved. |
| spellingShingle | Cannon, M Cheng, Q Kouvaritakis, B Rakovic, S Stochastic tube MPC with state estimation |
| title | Stochastic tube MPC with state estimation |
| title_full | Stochastic tube MPC with state estimation |
| title_fullStr | Stochastic tube MPC with state estimation |
| title_full_unstemmed | Stochastic tube MPC with state estimation |
| title_short | Stochastic tube MPC with state estimation |
| title_sort | stochastic tube mpc with state estimation |
| work_keys_str_mv | AT cannonm stochastictubempcwithstateestimation AT chengq stochastictubempcwithstateestimation AT kouvaritakisb stochastictubempcwithstateestimation AT rakovics stochastictubempcwithstateestimation |