Stochastic output feedback MPC with intermittent observations

Journal article

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.

Files:: Yan_et_al_2022_Stochastic output feedback.pdf

(Preview, Accepted manuscript, pdf, 508.0KB, Terms of use)

Language:: English
Keywords:: convex optimisation

FFR

model predictive control

chance constraints

output feedback

packet drops
Pubs id:: 1133564
Local pid:: pubs:1133564
Deposit date:: 2022-02-18

Copyright holder:: Elsevier
Notes:: This is the accepted manuscript version of the article. The final version is available online from Elsevier at: https://doi.org/10.1016/j.automatica.2022.110282

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