Robust Identification of LPV Time-Delay System with Randomly Missing Measurements

The robust parameter and output estimation for linear parameter varying (LPV) time-delay system with output data contaminated with outliers and subjected to randomly missing measurements are considered in this paper. The outliers, missing data, and the time-delay are widely existed in practical industry and have imposed extra difficulties on complex process modeling. The robust probability model to describe the LPV time-delay system is constructed with the student's {t} -distribution and the estimation problems are formulated in the framework of generalized expectation-maximization algorithm. The time-delay and parameter varying process properties, the outliers, and randomly missing measurements are taken into consideration comprehensively in the derivations of proposed algorithm and the unknown model parameters, scale parameter, degree of freedom parameter, the time-delay, and the noise-free output data are estimated simultaneously. The numerical example and a practical chemical process are used to present the efficacy of proposed algorithm.