Abstract
A discrete-time output error model identification method is proposed for industrial processes with time delay under unexpected load disturbance. By regarding the output response to load disturbance as a time-varying parameter for estimation, a least-squares identification algorithm is developed to simultaneously estimate all the model parameters including the time delay together with the load disturbance response. An auxiliary model is used to guarantee consistent estimation of the process model parameters. Moreover, dual forgetting factors are introduced to improve the convergence rates of estimating the model parameters and the load disturbance response, respectively. The convergence of parameter estimation is analyzed with a strict proof. A benchmark example from the literature is used to demonstrate the effectiveness and merit of the proposed identification method.
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