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A three-step identification procedure for ARARX models with additive measurement noise

This paper concerns the identification of extended ARARX models that consider also an additive white noise affecting the output. This model allows to take into account the presence of both a process disturbance and an additive measurement noise. A three-step identification procedure is described for identifying the extended ARARX model. The first step consists in an iterative bias-compensated least squares algorithm while the subsequent steps are based on simple (non-iterative) least squares equations. Simulation results are included to show the effectiveness of the proposed method.