This chapter describes and evaluates a statistically optimal method for the identification and estimation3 of continuous-time (CT) hybrid Box-Jenkins (BJ) transfer function models from discrete-time, sampled data. Here, the model of the basic dynamic system is estimated in continuous-time, differential equation form, while the associated additive noise model is estimated as a discrete-time, autoregressive moving average (ARMA) process. This refined instrumental variable method for continuous-time systems (RIVC) was first developed in 1980 by Young and Jakeman [52] and its simplest embodiment, the simplified RIVC (SRIVC) method, has been used successfully for many years, demonstrating the advantages that this stochastic formulation of the continuous-time estimation problem provides in practical applications (see, e.g., some recent such examples in [16, 34, 40, 45, 48]).
CITATION STYLE
Young, P. C., Garnier, H., & Gilson, M. (2008). Refined instrumental variable identification of continuous-time hybrid box-jenkins models. In Advances in Industrial Control (pp. 91–131). Springer International Publishing. https://doi.org/10.1007/978-1-84800-161-9_4
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