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 discounted cost subject to expectation constraint. It is assumed that sensor data lost known probability. Taking into account the losses modelled by Bernoulli process, we parameterise predicted policy as an affine function of future observations obtain convex linear-quadratic optimal problem. Constraint satisfaction bound are ensured without imposing bounds on distributions disturbance noise inputs. In addition, average long-run undiscounted closed loop shown be finite if discount factor takes appropriate values. We analyse robustness proposed respect possible uncertainties in arrival probability impact these constraint cost. Numerical simulations provided illustrate results.