Refined instrumental variable parameter estimation of continuous-time Box–Jenkins models from irregularly sampled data

摘要

This study investigates the estimation of continuous-time Box-Jenkins model parameters from irregularly sampled data. The Box-Jenkins structure has been successful in describing systems subject to coloured noise, since it contains two sub-models that feature the characteristics of both plant and noise systems. Based on plant-noise model decomposition, a two-step iterative procedure is proposed to solve the estimation problem, which consists of an instrumental variable method for the plant model and a prediction error method for the noise model. The proposed method is of low complexity and shows good estimation robustness and accuracy. Implementation issues are discussed to improve the computational efficiency. Numerical examples are presented to demonstrate the effectiveness of the proposed method.