Neural network control of the high-contrast imaging system

He Sun, N. Jeremy Kasdin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


Currently, linear state space modeling is used for focal plane wavefront estimation and control of high-contrast imaging system. Although this framework has made great strides in the past decades, it fails to track the nonlinearities from the deformable mirrors and the light propagation, which to some extent influences the accuracy of the electric field estimation and the speed and robustness of the controller. In this paper, we propose the application of neural networks to identify and optimally control a high-contrast imaging system. Based on the E-M algorithm and reinforcement learning techniques, we develop a new nonlinear system identificaton method and a corresponding nonlinear neural network controller. Simulation and experimental results from Princetons High Contrast Imaging Lab (HCIL) are reported to demonstrate the utility of this algorithm.

Original languageEnglish (US)
Title of host publicationSpace Telescopes and Instrumentation 2018
Subtitle of host publicationOptical, Infrared, and Millimeter Wave
EditorsGiovanni G. Fazio, Howard A. MacEwen, Makenzie Lystrup
ISBN (Print)9781510619494
StatePublished - 2018
EventSpace Telescopes and Instrumentation 2018: Optical, Infrared, and Millimeter Wave - Austin, United States
Duration: Jun 10 2018Jun 15 2018

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


OtherSpace Telescopes and Instrumentation 2018: Optical, Infrared, and Millimeter Wave
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


  • E-M algorithm
  • Exoplanet high-contrast imaging
  • Neural network
  • Reinforcement learning
  • Wavefront control and estimation


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