Robust Global Identification and Output Estimation for LPV Dual-Rate Systems Subjected to Random Output Time-Delays

This paper addresses the problems of robust global identification and fast-rate output estimation for linear parameter varying (LPV) dual-rate systems with output measurements subjected to random time-delays and outliers in statistical framework. In practical industry, the process data are often dual-rate sampled, and the output data are usually contaminated with outliers and may be subjected to uncertain time-delays due to lab analysis, long-distance or network transmission, etc. The LPV dual-rate model is given and the robust global identification and output estimation problems are formulated in statistical scheme with the Laplace distribution. The robust identification algorithm to estimate all the unknown parameters and output data are derived in the generalized expectation-maximization algorithm framework and the random time-delays and outliers in output data are handled adaptively in identification process. The proposed algorithm is presented and verified through numerical simulation and a practical chemical process.