Kalman-Filtering-Based Iterative Feedforward Tuning in Presence of Stochastic Noise: With Application to a Wafer Stage

Abstract

Iterative feedforward tuning (IFFT) enables high performance for motion systems that perform varying tasks without the need for system models. In this paper, IFFT is employed for a wafer stage to achieve good trajectory tracking performance and excellent disturbance compensation ability. Recently, the instrumental variable (IV) approach has been introduced into IFFT algorithms (IV-IFFT), enabling unbiased estimates for the parameters of a feedforward controller in the presence of stochastic noise. However, the estimation variances achievable with IV-IFFT are larger than zero. The aim of this paper is to develop an IFFT algorithm that enables unbiased estimates with zero asymptotic variances, which can be achieved by the simultaneous use of the Kalman filtering (KF) approach and the IV approach in IFFT, yielding the KF-IV-IFFT algorithm. The different roles of KF and IV approaches to improve the noise-tolerant capability of IFFT are also revealed. Experimental results obtained on a wafer stage confirm the practical relevance of the proposed KF-IV-IFFT algorithm.