Data-driven modeling and control of an X-ray bimorph adaptive mirror

被引:5
|
作者
Gunjala, Gautam [1 ,2 ,3 ]
Wojdyla, Antoine [2 ]
Goldberg, Kenneth A. [2 ]
Qiao, Zhi [3 ]
Shi, Xianbo [3 ]
Assoufid, Lahsen [3 ]
Waller, Laura [1 ]
机构
[1] Univ Calif Berkeley, Dept Elect Engn & Comp Sci, Berkeley, CA 94720 USA
[2] Lawrence Berkeley Natl Lab, Adv Light Source, Berkeley, CA 94720 USA
[3] Argonne Natl Lab, Adv Photon Source, Lemont, IL 60439 USA
关键词
adaptive optics; beamline optics; machine learning; control; X-rays; SHAPE;
D O I
10.1107/S1600577522011080
中图分类号
TH7 [仪器、仪表];
学科分类号
0804 ; 080401 ; 081102 ;
摘要
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 2 nm RMS. Using a prototype mirror and 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.
引用
收藏
页码:57 / 64
页数:8
相关论文
共 50 条
  • [1] COMPACT ACTIVE ADAPTIVE X-RAY MIRROR - BIMORPH PIEZOELECTRIC FLEXIBLE MIRROR
    SUSINI, J
    LABERGERIE, D
    ZHANG, L
    REVIEW OF SCIENTIFIC INSTRUMENTS, 1995, 66 (02): : 2229 - 2231
  • [2] Analysis of an x-ray mirror made from piezoelectric bimorph
    Zhang, Yao
    Li, Ming
    Tang, Shanzhi
    Gao, Junxiang
    Zhang, Weiwei
    Zhu, Peiping
    NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT, 2017, 860 : 13 - 18
  • [3] Fast optimization of a bimorph mirror using x-ray grating interferometry
    Wang, Hongchang
    Sawhney, Kawal
    Berujon, Sebastien
    Sutter, John
    Alcock, Simon G.
    Wagner, Ulrich
    Rau, Christoph
    OPTICS LETTERS, 2014, 39 (08) : 2518 - 2521
  • [4] Adaptive X-Ray Optics with a Deformable Mirror
    Kitamoto, Shunji
    Yamamoto, Norimasa
    Kohmura, Takayoshi
    Suga, Kazuharu
    Sekiguchi, Hiroyuki
    Sato, Jun'ichi
    Sudo, Keisuke
    Watanabe, Takeshi
    Ohkubo, Youhei
    Sekiguchi, Akiko
    Tsujimoto, Masahiro
    OPTICS FOR EUV, X-RAY, AND GAMMA-RAY ASTRONOMY II, 2005, 5900
  • [5] Fast shaping control of x ray beams using a closed-loop adaptive bimorph deformable mirror
    Alcock, Simon G.
    NIsTEA, Ioana-theodora
    Badami, Vivek G.
    Signorato, Riccardo
    FusCO, Matteo
    Hu, Lingfei
    Wang, Hongchang
    Sawhney, Kawal
    OPTICA, 2023, 10 (02): : 172 - 182
  • [6] Data-driven modeling and control of droughts
    Zaniolo, Marta
    Giuliani, Matteo
    Castelletti, Andrea
    IFAC PAPERSONLINE, 2019, 52 (23): : 54 - 60
  • [7] Probing lattice defects in crystalline battery cathode using hard X-ray nanoprobe with data-driven modeling
    Li, Jizhou
    Hong, Yanshuai
    Yan, Hanfei
    Chu, Yong S.
    Pianetta, Piero
    Li, Hong
    Ratner, Daniel
    Huang, Xiaojing
    Yu, Xiqian
    Liu, Yijin
    ENERGY STORAGE MATERIALS, 2022, 45 : 647 - 655
  • [8] Data-driven approach for synchrotron X-ray Laue microdiffraction scan analysis
    Song, Yintao
    Tamura, Nobumichi
    Zhang, Chenbo
    Karami, Mostafa
    Chen, Xian
    ACTA CRYSTALLOGRAPHICA A-FOUNDATION AND ADVANCES, 2019, 75 : 876 - 888
  • [9] Hyperspectral X-ray Denoising: Model-Based and Data-Driven Solutions
    Bonettini, NicolO
    Paracchini, Marco
    Bestagini, Paolo
    Marcon, Marco
    Tubaro, Stefano
    2019 27TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), 2019,
  • [10] OPTIMIZATION AND THEORETICAL PERFORMANCE OF AN ADAPTIVE X-RAY MIRROR
    SUSINI, J
    FORSTNER, G
    ZHANG, L
    BOYER, C
    RAVELET, R
    REVIEW OF SCIENTIFIC INSTRUMENTS, 1992, 63 (01): : 423 - 427