Robust tracking with FPGA for high-speed laser galvanometer scanning

A galvanometer scanner is a key component for laser beam steering/scanning thanks to its fast-response and high accuracy. In order to enhance the processing quality and efficiency of galvanometer, this paper proposes a robust control method for galvanometer scanners to reduce the tracking error in high speed processing of general patterns. A radial basis function (RBF) neural network is utilized to deal with the nonlinearity and the parameter-variation in galvanometer scanners, mainly caused by frictions and temperature drifts. And a feedforward control is integrated to meet the requirement of fast-response. For possible industrial applications, a prototype is built with a field programmable gate array (FPGA) hardware. The detailed implementation of the RBF robust controller with high calculation accuracy and low usages of resources are then described. Various experiments are conducted on this prototype to validate the proposed control method.