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...
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| Published in | Automatica (Oxford) Vol. 141; p. 110282 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.07.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0005-1098 1873-2836 |
| DOI | 10.1016/j.automatica.2022.110282 |
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| Abstract | 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. |
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| AbstractList | 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. |
| ArticleNumber | 110282 |
| Author | Goulart, Paul J. Cannon, Mark Yan, Shuhao |
| Author_xml | – sequence: 1 givenname: Shuhao surname: Yan fullname: Yan, Shuhao email: sy499@cornell.edu organization: School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, 14853, USA – sequence: 2 givenname: Mark surname: Cannon fullname: Cannon, Mark email: mark.cannon@eng.ox.ac.uk organization: Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK – sequence: 3 givenname: Paul J. surname: Goulart fullname: Goulart, Paul J. email: paul.goulart@eng.ox.ac.uk organization: Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK |
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| Keywords | Model predictive control Chance constraints Packet drops Convex optimisation Output feedback |
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| SubjectTerms | Chance constraints Convex optimisation Model predictive control Output feedback Packet drops |
| Title | Stochastic output feedback MPC with intermittent observations |
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