System identification using slow and irregular output samples

Identification of systems with slowly and irregularly sampled output data is considered. The identified models can be used in inferential control or as soft sensor. Dual-rate system with slow output samples is a special case of the problem studied here. In this work, it will be show that an output error method is a natural choice for the problem that can identify the fast rate model directly from the fast input and slow and irregular output data. When the system is in the model set, consistence of the output error model is established and minimum variance property is proved. When the model order is lower than the true order, bias distribution in the frequency domain is given. Simulation studies and industrial data will be used to illustrate the method.