Data-driven feedforward decoupling filter design for parallel Nanopositioning stages

Abstract

Cross-coupling effect severely hinder fast and accurate tracking for parallel piezo nanopositioning stages. In this paper, a data-driven feedforward decoupling filter (DDFDF) is proposed to reduce the cross-coupling caused errors. Traditional control methods for coupled system could achieve good performance on the premise that the dynamic model is accurate and no non-minimum phase zeros exist. The proposed method is totally data-driven with the advantage of no need for accurate identified model and model structure by Gauss-Newton gradient-based algorithm. The DDFDF for eliminating cross-coupling errors was verified on a 2-DOF coupled nanopositioning stage through simulations. Results show the effectiveness of the proposed controller by comparing with open-loop simulations and the well-designed feedback controller.