Controlling the delivery of kHz-class pulsed lasers is of interest in a
variety of industrial and scientific applications, from
next-generation laser-plasma acceleration to laser-based x-ray
emission and high-precision manufacturing. The transverse position of
the laser pulse train on the application target is often subject to
fluctuations by external drivers (e.g., room cooling and
heating systems, motorized optics stages and mounts, vacuum systems,
chillers, and/or ground vibrations). For typical situations where the
disturbance spectrum exhibits discrete peaks on top of a
broad-bandwidth lower-frequency background, traditional PID
(proportional-integral-derivative) controllers may struggle, since as
a general rule PID controllers can be used to suppress vibrations up
to only about 5%–10% of the sampling frequency. Here, a predictive
feed-forward algorithm is presented that significantly enhances the
stabilization bandwidth in such laser systems (up to the Nyquist limit
at half the sampling frequency) by online identification and filtering
of one or a few discrete frequencies using optimized Fourier filters.
Furthermore, the system architecture demonstrated here uses
off-the-shelf CMOS cameras and piezo-electric actuated mirrors
connected to a standard PC to process the alignment images and
implement the algorithm. To avoid high-end, high-cost components, a
machine-learning-based model of the piezo mirror’s dynamics was
integrated into the system, which enables high-precision positioning
by compensating for hysteresis and other hardware-induced effects. A
successful demonstration of the method was performed on a 1 kHz laser
pulse train, where externally-induced vibrations of up to 400 Hz were
attenuated by a factor of five, far exceeding what could be done with
a standard PID scheme.