The surface accuracy of a large radio telescope’s primary reflector is easily affected by gravity and temperature change during observations. An active surface system is crucial to ensure the regular operation and high-quality data output of the radio telescope. We propose a real-time closed-loop active surface system including two components. The first component, a new type of photoelectric edge sensor, detects the angle change of the adjacent panels. The second component, the displacement actuator, adjusts the panels’ position and posture to compensate for the angle changes. So, over the entire observation, the closed-loop surface control system with these two components could actively maintain the primary reflector’s accuracy in real time. Using this approach, we constructed an experimental active surface system for the Xinjiang Qitai 110 m Radio Telescope (QTT) to test the maintenance of the surface accuracy. The angle measurement accuracy is better than 0.″2, and the positioning accuracy of the displacement actuator could achieve ±15 μm over the whole 50 mm stroke. The preliminary test results show that the accuracy requirements of the QTT’s primary reflector surface can be met using the active surface system we propose.

Co-phase and co-focus detection is one of the key technologies for large-aperture segmented mirror telescopes. In this paper, a new edge sensor based on fringes of equal thickness is developed, which can detect each segment’s relative piston, tilt, and tip errors from the interferograms. Based on the co-focus demand for many ground-based seeing limited segmented mirror telescopes, an edge sensor prototype based on such a principle is built and applied in the indoor segmented mirror experiment system in the lab. According to the co-focus requirement of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, many simulations and experiments are carried out for co-focus error detection of the segmented mirror system. Experiment results show that the co-focus accuracy is better than 0.″02 rms, which can meet the co-focus requirements of most large or extremely large segmented mirror astronomical telescopes.

Research on adaptive deformable mirror technology for voice coil actuators (VCAs) is an important trend in the development of large ground-based telescopes. A voice coil adaptive deformable mirror contains a large number of actuators, and there are problems with structural coupling and large temperature increases in their internal coils. Additionally, parameters of the traditional proportional integral derivative (PID) control cannot be adjusted in real-time to adapt to system changes. These problems can be addressed by introducing fuzzy control methods. A table lookup method is adopted to replace real-time calculations of the regular fuzzy controller during the control process, and a prototype platform has been established to verify the effectiveness and robustness of this process. Experimental tests compare the control performance of traditional and fuzzy proportional integral derivative (Fuzzy-PID) controllers, showing that, in system step response tests, the fuzzy control system reduces rise time by 20.25%, decreases overshoot by 78.24%, and shortens settling time by 67.59%. In disturbance rejection experiments, fuzzy control achieves a 46.09% reduction in the maximum deviation, indicating stronger robustness. The Fuzzy-PID controller, based on table lookup, outperforms the standard controller significantly, showing excellent potential for enhancing the dynamic performance and disturbance rejection capability of the voice coil motor actuator system.

The 12-m optical infrared telescope is a proposed large-scale general-purpose telescope in China. This telescope will integrate Ground Layer Adaptive Optics (GLAO) to effectively mitigate the effects of atmosphere turbulence within a 14′ field of view, thereby enhancing observational efficiency and scientific performance. To systematically assess the correction capabilities of the 12-m telescope with GLAO under various error conditions, simulations were conducted using the Durham Adaptive Optics Simulation Program. The correction performance is highly dependent on the atmospheric turbulence model, guide star, and wavefront sensor system configurations. Under typical site conditions with seeing of 0.8″, the GLAO system can correct ∼70% turbulence in the ground layer, resulting in a full width at half maximum (FWHM) of 0.35″ at 840 nm and 0.27″ at 1200 nm, corresponding to correction gains of 1.6× and 1.9×, respectively. The system also achieves a 1.3× improvement in half-light radius (θ50) and 2.0× enhancement in encircled energy within 200 milliarcseconds (EE200 mas). Additional system aberrations, including system pupil shape, phasing, alignment, and noncommon path aberrations, introduce measurable performance degradation: increasing FWHM by 10.0% (840 nm) and 3.0% (1200 nm), θ50 by 15.0% and 8.7%, and reducing EE200 mas by 24.0% and 9.4%, respectively. We also investigate the impact of large-aperture adaptive deformable mirrors, simulated by finite element software, on the performance of GLAO system. Key factors such as influence function, conjugate height, tilt angle and off-zenith observation are considered.

Shack–Hartmann wavefront sensors (SHWFSs) are crucial for detecting distortions in adaptive optics systems, but the accuracy of wavefront reconstruction is often hampered by low guide star brightness or strong atmospheric turbulence. This study introduces a new method of using the Vision Transformer model to process image information from SHWFSs. Compared with previous traditional methods, this model can assign a weight value to each subaperture by considering the position and image information of each subaperture of this sensor, and it can process to obtain wavefront reconstruction results. Comparative evaluations using simulated SHWFS light intensity images and corresponding deformable mirror command vectors demonstrate the robustness and accuracy of the Vision Transformer under various guide star magnitudes and atmospheric conditions, compared to convolutional neural networks (CNNs), represented in this study by Residual Neural Network (ResNet), which are widely used by other scholars. Notably, normalization preprocessing significantly improves the CNN performance (improving Strehl ratio by up to 0.2 under low turbulence) while having a varied impact on the Vision Transformer, improving its performance under a low turbulence intensity and high brightness (Strehl ratio up to 0.8) but deteriorating under a high turbulence intensity and low brightness (Strehl ratio reduced to about 0.05). Overall, the Vision Transformer consistently outperforms CNN models across all tested conditions, enhancing the Strehl ratio by an average of 0.2 more than CNNs.