On-sky demonstration of optimal control for adaptive optics at Palomar Observatory

Fuzzy control algorithm for adaptive optical systems

In this study, the direct wavefront gradient algorithm is used to calculate the control voltage of the deflection mirror. Considering the control voltage as the input of the fuzzy controller, the fuzzy rule base is established and the three parameters of the proportional-integral-derivative controller are obtained using dual fuzzy control. In addition, a weight factor and threshold judgment are included to correct the control voltage and realize parameter self-tuning. A simulation and experiments demonstrate that this method can effectively correct the wavefront distortion signal and improve the system response speed. Moreover, it can reduce the difficulty of parameter adjustment and meet the requirements of laser communication for wavefront correction.

Deep learning control model for adaptive optics systems

To correct wavefront aberrations, commonly employing proportional-integral control in adaptive optics (AO) systems, the control process depends strictly on the response matrix of the deformable mirror. The alignment error between the Hartmann-Shack wavefront sensor and the deformable mirror is caused by various factors in AO systems. In the conventional control method, the response matrix can be recalibrated to reduce the impact of alignment error, but the impact cannot be eliminated. This paper proposes a control method based on a deep learning control model (DLCM) to compensate for wavefront aberrations, eliminating the dependence on the deformable mirror response matrix. Based on the wavefront slope data, the cost functions of the model network and the actor network are defined, and the gradient optimization algorithm improves the efficiency of the network training. The model network guarantees the stability and convergence speed, while the actor network improves the control accuracy, realizing an online identification and self-adaptive control of the system. A parameter-sharing mechanism is adopted between the model network and the actor network to control the system gain. Simulation results show that the DLCM has good adaptability and stability. Through self-learning, it improves the convergence accuracy and iterations, as well as the adjustment tolerance of the system.

Predictive dynamic digital holography and image sharpening

Digital holography is often combined with image sharpening for wavefront estimation and correction, and this combination has received recent attention for many imaging and sensing applications. A significant obstacle for digital holography and image sharpening in high-speed real-time applications is the fact that the process is computationally intensive, requiring iterative virtual wavefront propagation and hill-climbing algorithms to optimize sharpness criteria. This paper introduces a method for accelerating dynamic digital holography and image sharpening by wavefront prediction. The approach here integrates optimal state-space prediction filters with digital holography and image sharpening to short-circuit the computationally intensive process of virtual wavefront propagation and sharpness optimization.