SEQUENTIAL PREDICTION-ERROR METHOD FOR STRUCTURAL IDENTIFICATION

Time-domain methods for the identification of linear structural dynami c systems are studied. The stochastic autoregressive and moving averag e (ARMAX) model is used to process the measured excitation and respons e records contaminated by noises. The study focuses on the sequential prediction-error method incorporating several techniques for improving the parameter estimation. They are the exponential data weighting, th e global data weighting, and the square-root estimation techniques. Ef ficient procedures of the square-root estimation are developed for the multi-input and multioutput (MIMO) case as well as the multi-input an d single-output (MISO) case. Verifications of the present methods are carried out using the simulated time histories for the input excitatio n and output response, as well as using the experimental data on a bui lding model. The results indicate that the square-root estimation tech nique is particularly effective for improving the convergence and accu racy of the sequential estimation, even with crude initial guesses.