Robust Variational Inference for LPV Dual-Rate Systems With Randomly Delayed Outputs

Abstract

This article proposes a variational Bayesian (VB) approach for the identification of linear parameter-varying (LPV) dual-rate systems when the measured data are contaminated with varying time delays and outliers. By treating all the unknown parameters as hidden variables and imposing suitable priors, the full Bayesian model for the identification problem can be established. A modified robust Kalman filter is adopted to estimate the missing outputs required in the regressor, and the VB algorithm is employed to simultaneously estimate the LPV model parameters with their uncertainties, time delays with their significances, and noise-free process outputs. One additional advantage of the proposed method is that the optimal interval of time delays can be determined automatically with the insignificant time delays suppressed. The validity of the developed approach is illustrated through a numerical study and the electronic bandpass filter benchmark.