A linear parameter varying (LPV) method can develop the model of the multi-stage

non-linear process through the identification of linear multiple models. In recent years it has been of great concern. This paper investigates the identification of the LPV model with incomplete measurements. First

to indicate whether the output measurement is missing at each sampling instant

a binary variable is defined through which the availability of the output measurement is denoted. The key variable(s) can be taken as the scheduling variable(s) and the main operating point is determined based on the actual production process. Then

local models are constructed around each operating point

and EM algorithm is introduced to estimate their parameters. Both the missing data and the sampling data belonging are treated as the hidden variables. Finally

local models are combined according to an exponential weighting function. A simulated numerical example as well as the continuous stirred tank reactor (CSTR) are employed to demonstrate the effectiveness of the proposed method.