The reliability of Free Space Optical (FSO) communications between a ground station and celestial objects is significantly hampered by the variability in atmospheric conditions. Enhancing the system’s capabilities to recover the received signal can significantly increase the robustness and broaden the operational scope of this type of communication. One of the most promising avenues for improvement entails integrating Adaptive Optics systems with the latest Machine Learning techniques. We study different control laws based on a classical integrator, a LQG with a Kalman filter (with a second order autoregressive model) and a Reinforcement Learning approach: we evaluate the performance of the three control laws with the Strehl ratio.
The rise of exoplanet research and the growing need for high-capacity free-space optical communication links have placed high demands on the performance of adaptive optics (AO) systems. A key challenge lies in mitigating temporal error, this imposes severe time constraints on the response of deformable mirrors (DM). Recent advancements at ALPAO in the realm of input shaping techniques have yielded promising results, effectively eliminating oscillations and reducing overshoot from 60 to less than 5%. Both, rise time and settling time have been diminished to below 50μs, representing an improvement of one order of magnitude compared to the unshaped case. The solution found is compatible with real-time computing constraints and can be integrated in the DM drive electronics or in separate processing unit.
The Provence Adaptive optics Pyramid Run System (PAPYRUS) is a pyramid-based Adaptive Optics (AO) system that will be installed at the Coude focus of the 1.52m telescope (T152) at the Observatoire de Haute Provence (OHP). The project is being developed by PhD students and Postdocs across France with support from staff members consolidating the existing expertise and hardware into an RD testbed. This testbed allows us to run various pyramid wavefront sensing (WFS) control algorithms on-sky and experiment on new concepts for wavefront control with additional benefit from the high number of available nights at this telescope. It will also function as a teaching tool for students during the planned AO summer school at OHP. To our knowledge, this is one of the first pedagogic pyramid-based AO systems on-sky. The key components of PAPYRUS are a 17x17 actuators Alpao deformable mirror with a Alpao RTC, a very low noise camera OCAM2k, and a 4-faces glass pyramid. PAPYRUS is designed in order to be a simple and modular system to explore wavefront control with a pyramid WFS on sky. We present an overview of PAPYRUS, a description of the opto-mechanical design and the current status of the project.
KEYWORDS: Adaptive optics, Wavefront sensors, Cameras, Deformable mirrors, Wavefronts, Actuators, System integration, Sensors, Electron multiplying charge coupled devices, Control systems
An adaptive optics system running at 1500 Hz was integrated using commercially available components. The deformable mirror was made by Alpao and has 277 actuators on a 1:5mm pitch. The wavefront sensor is based on the OCAM2 EMCCD (Electron-multiplying charge-coupled device) camera from First Light Imaging and a 20×20 lenslet array. We present an initial system integration phase using the Alpao Core Engine toolbox running in a Matlab® environment. During the second integration phase, benchmark tests for Alpao's real-time controller ACEfast show the possibility to obtain a pure delay of τ = 130 µs in a parallel worker configuration with a computing power of 2 CPU/8 core + 4GPU for a problem size equivalent to state-of-the-art adaptive optics systems.
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