Moving horizon state estimation for constrained networked control systems with missing measurements

This paper is concerned with the state estimation problem for constrained networked control systems with missing measurements due to a lossy network between sensor and controller. Based on a linear model of packet dropouts with stochastic parameters, a novel moving horizon state estimator combining with a local observer is proposed to deal with the uncertainty from a lossy network. The existence of the boundness on the square norm of the estimation error affected by the packet arrival probability is also proved. It is further analyzed that in absence of the noises, the square norm of the estimation error exponentially converges to zero. Finally, a numerical example is given to demonstrate the effectiveness of the proposed method, compared with the general moving horizon method.