Stochastic output feedback MPC with intermittent observations
This paper designs a model predictive control (MPC) law for constrained linear systems with stochastic additive disturbances and noisy measurements, minimising a discounted cost subject to a discounted expectation constraint. It is assumed that sensor data is lost with a known probability. Taking in...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
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Elsevier
2022
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| _version_ | 1826309782459383808 |
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| author | Yan, S Cannon, M Goulart, PJ |
| author_facet | Yan, S Cannon, M Goulart, PJ |
| author_sort | Yan, S |
| collection | OXFORD |
| description | This paper designs a model predictive control (MPC) law for constrained linear systems with stochastic additive disturbances and noisy measurements, minimising a discounted cost subject to a discounted expectation constraint. It is assumed that sensor data is lost with a known probability. Taking into account the data losses modelled by a Bernoulli process, we parameterise the predicted control policy as an affine function of future observations and obtain a convex linear-quadratic optimal control problem. Constraint satisfaction and a discounted cost bound are ensured without imposing bounds on the distributions of the disturbance and noise inputs. In addition, the average long-run undiscounted closed loop cost is shown to be finite if the discount factor takes appropriate values. We analyse robustness of the proposed control law with respect to possible uncertainties in the arrival probability of sensor data and we bound the impact of these uncertainties on constraint satisfaction and the discounted cost. Numerical simulations are provided to illustrate these results. |
| first_indexed | 2024-03-07T07:40:53Z |
| format | Journal article |
| id | oxford-uuid:65493a65-7eb6-4897-9f30-bc51e091266c |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:40:53Z |
| publishDate | 2022 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:65493a65-7eb6-4897-9f30-bc51e091266c2023-04-20T10:05:35ZStochastic output feedback MPC with intermittent observationsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:65493a65-7eb6-4897-9f30-bc51e091266cEnglishSymplectic ElementsElsevier2022Yan, SCannon, MGoulart, PJThis paper designs a model predictive control (MPC) law for constrained linear systems with stochastic additive disturbances and noisy measurements, minimising a discounted cost subject to a discounted expectation constraint. It is assumed that sensor data is lost with a known probability. Taking into account the data losses modelled by a Bernoulli process, we parameterise the predicted control policy as an affine function of future observations and obtain a convex linear-quadratic optimal control problem. Constraint satisfaction and a discounted cost bound are ensured without imposing bounds on the distributions of the disturbance and noise inputs. In addition, the average long-run undiscounted closed loop cost is shown to be finite if the discount factor takes appropriate values. We analyse robustness of the proposed control law with respect to possible uncertainties in the arrival probability of sensor data and we bound the impact of these uncertainties on constraint satisfaction and the discounted cost. Numerical simulations are provided to illustrate these results. |
| spellingShingle | Yan, S Cannon, M Goulart, PJ Stochastic output feedback MPC with intermittent observations |
| title | Stochastic output feedback MPC with intermittent observations |
| title_full | Stochastic output feedback MPC with intermittent observations |
| title_fullStr | Stochastic output feedback MPC with intermittent observations |
| title_full_unstemmed | Stochastic output feedback MPC with intermittent observations |
| title_short | Stochastic output feedback MPC with intermittent observations |
| title_sort | stochastic output feedback mpc with intermittent observations |
| work_keys_str_mv | AT yans stochasticoutputfeedbackmpcwithintermittentobservations AT cannonm stochasticoutputfeedbackmpcwithintermittentobservations AT goulartpj stochasticoutputfeedbackmpcwithintermittentobservations |