Robust Multimodel Identification of LPV Systems With Missing Observations Based on t-Distribution

A robust multiple model strategy for linear parameter varying (LPV) systems identification relied on Student's t-distribution is studied in this article. Since most industrial processes operate over several working points to meet multiple production objectives, a single model is generally insufficient to describe the process behavior over the whole operating range. To cope with this issue, the multiple model strategy is employed and at the same time, the outliers and missing measurements problems are both considered in this article. The finite impulse response (FIR) model is chosen to describe the local behavior of the LPV system. The statistical scheme based on the t-distribution is utilized to model the system noise so as to deal with the outliers. The problem of parameters estimation with incomplete outputs is addressed by using the expectation-maximization (EM) algorithm. The feasibility and effectiveness of the developed approach are proved via a mechanical unit.