Event-Based Measurement Updating Kalman Filter in Network Control Systems

An event based measurement updating method is introduced for discrete Kalman filters to estimate the state feedback of Lebesgue sampled data systems. It is proposed that the conventional prediction and the measurement updating stages of discrete Kalman filters are not processed at the same rate. The prediction can be constant but the measurement rate is varied based on Lebesgue sampling. The measurement update portion is executed when an event takes place. The stability of discrete Kalman filters is investigated when the Lebesgue threshold is increased.