Novel estimation method of current state variables for time delay system using a Kalman filter with dual models-influence of modeling error

The article concerns remote measurement. Aiming to obtain current state variables of a mechanical system with time delay, we estimated current state variables from the delayed measurement signal by using a Kalman filter. In order to increase the stability of the estimation system, we have proposed a novel Kalman filter with dual mathematical models, and also have shown that it was very effective because it was more robust and precise than a conventional Kalman filter with a single model. We have examined an influence for the estimation error that was caused by modeling errors of the plant, and have examined one for the sampling time error. From these results it is safer to set the variance of the measurement noise and input noise higher if uncertain, because the increment of the estimation error is smaller. Moreover the increment of the estimation error caused by the sampling time error keeps almost constant from the experiment and the simulation, when the sampling time is set longer