Intelligent GRU-RIC Position-Loop Feedforward Compensation Control Method With Application to an Ultraprecision Motion Stage

Abstract

In the realm of ultraprecision motion control, achieving high tracking accuracy, great trajectory generalization, and robust disturbance rejection simultaneously remains a challenge. This article proposes an intelligent gated recurrent unit real-time iterative compensation (GRU-RIC) position-loop feedforward compensation control method to tackle this issue. Specifically, a gated recurrent unit (GRU) neural network is first trained to accurately predict tracking errors for given reference trajectories in advance. The predicted error serves as an offline feedforward compensation signal to enhance tracking accuracy. To mitigate residual tracking errors arising from incomplete offline compensation and unexpected disturbances, a real-time iterative compensation (RIC) scheme that generates optimal online feedforward signals through real-time system prediction is proposed. Both offline and online compensation signals are synergistically applied to modify the reference trajectory in a position-loop feedforward manner. Experimental results on a nano-precision air-bearing motion stage confirm that the GRU-RIC method achieves 10-nm tracking accuracy, which is the same as iterative learning control (ILC), while outperforming ILC in trajectory generalization and disturbance rejection.