May 20, 2022 Presentation Open Access

Pou, Bartomeu; Smith, Jeffrey; Quinones, Eduardo; Martin, Mario; Gratadour, Damien

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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.6567127", 
  "language": "eng", 
  "title": "A Non Linear Control Method with Reinforcement Learning for Adaptive Optics with Pyramid Sensors", 
  "issued": {
    "date-parts": [
      [
        2022, 
        5, 
        20
      ]
    ]
  }, 
  "abstract": "<p>Extreme Adaptive Optics (AO) systems are designed to provide high resolution and high contrast observing capabilities on the largest ground-based telescopes through exquisite phase reconstruction accuracy. In that context, the pyramid wavefront sensor (P-WFS) has shown promise to deliver the means to provide such accuracy due to its high sensitivity. However, traditional methods cannot leverage the highly non-linear P-WFS measurements to their full potential. We present a predictive control method based on Reinforcement Learning (RL) for AO control with a P-WFS. The proposed approach is data-driven, has no assumptions about the system&#39;s evolution, and is non-linear due to the usage of neural networks. First, we discuss the challenges of using an RL control method with a P-WFS and propose solutions. Then, we show that our method outperforms an optimized integrator controller. Finally, we discuss its possible path for an actual implementation.</p>", 
  "author": [
    {
      "family": "Pou, Bartomeu"
    }, 
    {
      "family": "Smith, Jeffrey"
    }, 
    {
      "family": "Quinones, Eduardo"
    }, 
    {
      "family": "Martin, Mario"
    }, 
    {
      "family": "Gratadour, Damien"
    }
  ], 
  "type": "speech", 
  "id": "6567127"
}