Predictive Tracking Under Persisten Disturbances and Data Errors Using H2 FIR Approach

Industrial processes may incur a significant loss in information under unspecified impacts and data errors. Therefore, robust predictors are required to ensure the performance. In this article, we design an II, optimal finite impulse response (H-2-OFIR) predictor under persistent disturbances, measurement errors, and initial errors. The H-2-OFIR predictor is derived by minimizing the squared weighted Frobenius norms for each error. A suboptimal H-2 finite impulse response (FIR) prediction algorithm is obtained using a linear matrix inequality. The H-2-OFIR predictive tracker is tested by simulations assuming Markov disturbances and data errors driven by the Gaussian, uniform, and industrial Cauchy heavy-tailed noise. It is shown experimentally that in predictive tracking of a moving robot using the ultrawideband technology, the H-2-OFIR predictor operating with full error matrices is more robust than the Kalman and unbiased FIR predictors.

Automated summary

This article presents a robust predictor for industrial processes that ensures performance even under unspecified impacts and data errors. The predictor is tested in predictive tracking of a moving robot using ultrawideband technology, and it is found to be more robust than other predictors in various noise conditions.