Journal papers
Gautam Gunjala; Antoine Wojdyla; Kenneth A. Goldberg; Zhi Qiao; Xianbo Shi; Lahsen Assoufid; Laura Waller
Data-driven modeling and control of an X-ray bimorph adaptive mirror Journal Article
In: Journal of Synchrotron Radiation, vol. 30, no. 1, 2023.
Abstract | Links | BibTeX | Tags: adaptive optics, beamline optics, x ray imaging
@article{Gunjala:tv5041,
title = {Data-driven modeling and control of an X-ray bimorph adaptive mirror},
author = {Gautam Gunjala and Antoine Wojdyla and Kenneth A. Goldberg and Zhi Qiao and Xianbo Shi and Lahsen Assoufid and Laura Waller},
url = {https://doi.org/10.1107/S1600577522011080},
doi = {10.1107/S1600577522011080},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Journal of Synchrotron Radiation},
volume = {30},
number = {1},
abstract = {Adaptive X-ray mirrors are being adopted on high-coherent-flux synchrotron and X-ray free-electron laser beamlines where dynamic phase control and aberration compensation are necessary to preserve wavefront quality from source to sample, yet challenging to achieve. Additional difficulties arise from the inability to continuously probe the wavefront in this context, which demands methods of control that require little to no feedback. In this work, a data-driven approach to the control of adaptive X-ray optics with piezo-bimorph actuators is demonstrated. This approach approximates the non-linear system dynamics with a discrete-time model using random mirror shapes and interferometric measurements as training data. For mirrors of this type, prior states and voltage inputs affect the shape-change trajectory, and therefore must be included in the model. Without the need for assumed physical models of the mirror's behavior, the generality of the neural network structure accommodates drift, creep and hysteresis, and enables a control algorithm that achieves shape control and stability below 2nm RMS. Using a prototype mirror and it ex situ metrology, it is shown that the accuracy of our trained model enables open-loop shape control across a diverse set of states and that the control algorithm achieves shape error magnitudes that fall within diffraction-limited performance.},
keywords = {adaptive optics, beamline optics, x ray imaging},
pubstate = {published},
tppubtype = {article}
}
Adaptive X-ray mirrors are being adopted on high-coherent-flux synchrotron and X-ray free-electron laser beamlines where dynamic phase control and aberration compensation are necessary to preserve wavefront quality from source to sample, yet challenging to achieve. Additional difficulties arise from the inability to continuously probe the wavefront in this context, which demands methods of control that require little to no feedback. In this work, a data-driven approach to the control of adaptive X-ray optics with piezo-bimorph actuators is demonstrated. This approach approximates the non-linear system dynamics with a discrete-time model using random mirror shapes and interferometric measurements as training data. For mirrors of this type, prior states and voltage inputs affect the shape-change trajectory, and therefore must be included in the model. Without the need for assumed physical models of the mirror's behavior, the generality of the neural network structure accommodates drift, creep and hysteresis, and enables a control algorithm that achieves shape control and stability below 2nm RMS. Using a prototype mirror and it ex situ metrology, it is shown that the accuracy of our trained model enables open-loop shape control across a diverse set of states and that the control algorithm achieves shape error magnitudes that fall within diffraction-limited performance.