Open AccessOne-Step Predictive H2 FIR Tracking under Persistent Disturbances and Data Errors
Author(s) -
Oscar Ibarra-Manzano,
José A. Andrade-Lucio,
Yuriy S. Shmaliy,
Yuan Xu
Publication year - 2021
Publication title -
wseas transactions on signal processing
Language(s) - English
Resource type - Journals
eISSN - 2224-3488
pISSN - 1790-5052
DOI - 10.37394/232014.2021.17.12
Subject(s) - robustness (evolution) , kalman filter , finite impulse response , control theory (sociology) , computer science , model predictive control , tracking error , algorithm , mathematics , statistics , artificial intelligence , control (management) , gene , biochemistry , chemistry
Information loss often occurs in industrial processes under unspecified impacts and data errors. Therefore robust predictors are required to assure the performance. We design a one-step H2 optimal finite impulse response (H2-OFIR) predictor under persistent disturbances, measurement errors, and initial errors by minimizing the squared weighted Frobenius norms for each error. The H2-OFIR predictive tracker is tested by simulations assuming Gauss-Markov disturbances and data errors. It is shown that the H2-OFIR predictor has a better robustness than the Kalman and unbiased FIR predictor. An experimental verification is provided based on the moving robot tracking problem