One-Step Predictive H2 FIR Tracking under Persistent Disturbances and Data Errors

Abstract

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