Real-time robust identification algorithm for structural systems with time-varying dynamic characteristics

By adding a function of memory fading for past observation data to the Kalman filter which has often been used as a time marching identification algorithm we developed an adaptive Kalman filter scheme. The rate of memory fading was defined by a forgetting factor multiplying to pre- information term at each time step. In order to track fast variation in the system parameters the value of forgetting factor should be small. On the other hand, to remove the random noise from the signal, the number of sample points used at any time should be large enough, that is, the large value of forgetting factor should be used. There is, therefore, a trade-off between the time-tracking ability and the noise sensitivity of the identification. The Akaike- Bayes Information Criteria was applied to determine the optimal forgetting factor. Applications of the newly developed identification algorithm to a multi-degree of structural system with non-stationary dynamic characteristic worked out well.