Noise Reduction of Learning Control for Periodic Motion of Galvanometer Scanner

摘要

For highly precise motion of a galvanometer scanner that tracks a periodic motion reference, learning control significantly decreases the tracking error. To achieve higher quality motion by reducing the angular sensor noise, this paper investigates inversion-based iterative control (IIC) that can learn only at the fundamental and harmonic frequencies of the periodic motion reference. This enables to separate the compensable tracking error from the noise to be eliminated during learning in the frequency domain. The analysis in the paper reveals a tradeoff for the noise reduction in the IIC design, and this paper proposes an equation to quickly tune a design parameter in the tradeoff for better performance. Furthermore, the effectiveness of the IIC algorithm is experimentally demonstrated for a galvanometer scanner. When the galvanometer scanner tracks a 20 Hz triangular motion of ± 10 degrees, the IIC successfully decreases the residual tracking error by 41 % to 2.83 ×10-4deg, by utilizing the noise reduction.