Gain Scheduling for Sampled-data State Estimation over Lossy Networks

Abstract

This paper proposes a design method for sampled-data state estimator over lossy networks. When a measurement signal loss is detected, the proposed estimator switches gains and continues to update the estimate based upon last received measurement. The gains are computed in advance by means of a common solution of linear matrix inequalities so that the estimation is suboptimal with respect to the mean square error in the steady state. The effectiveness of the proposed method is illustrated/evaluated via numerical simulations.