A tip-tilt mirror (TTM) control method is designed to enhance the control bandwidth and ensure the rejection performance of the adaptive optics (AO) tip-tilt correction system. Optimized with the Smith predictor and filter, linear active disturbance rejection (LADRC) is adopted to achieve the tip-tilt correction. An AO tip-tilt correction experimental platform was built to validate the method. Experimental results show that the proposed method improves the control bandwidth of the system by at least 3.6 times compared with proportional–integral (PI) control. In addition, under the same control bandwidth condition, compared with the Smith predictor and proportional–integral (PI–Smith) control method, the system is more capable of rejecting internal and external disturbances, and its dynamic response performance is improved by more than 29%.

To resolve cooperative control issues of dual wavefront correctors in generalized irregular pupil regions, we propose a decoupling control algorithm based on numerical orthogonal polynomials (NOP). The proposed algorithm firstly deduces NOP from Zernike polynomials in generalized irregular pupil regions. Then, according to wavefront restoration, different spatial frequency aberrations to different wavefront correctors are assigned precisely. Finally, the algorithm calculates and eliminates the cross-coupling between dual wavefront correctors. As observed in numerical simulations and experiments based on a typical woofer-tweeter (W-T) adaptive optics system, NOP decoupling control algorithm restrains the cross-coupling between woofer and tweeter in generalized irregular pupil regions. Moreover, there are obvious advantages over Zernike polynomials decoupling control algorithm in cross-coupling suppression for various scenarios in irregular pupil regions and restoration orders.

The tip–tilt mirror (TTM) is an important component of adaptive optics (AO) to achieve beam stabilization and pointing tracking. In many practical applications, the information of accurate TTM dynamics, complete system state, and noise characteristics is difficult to achieve due to the lack of sufficient sensors, which then restricts the implementation of high precision tracking control for TTM. To this end, this paper proposes a new method based on noisy-output feedback Q-learning. Without relying on neural networks or additional sensors, it infers the dynamics of the controlled system and reference jitter using only noisy measurements, thereby achieving optimal tracking control for the TTM system. We have established a modified Bellman equation based on estimation theory, directly linking noisy measurements to system performance. On this basis, a fast iterative learning of the control law is implemented through the adaptive transversal predictor and experience replay technique, making the algorithm more efficient. The proposed algorithm has been validated with an application to a TTM tracking control system, which is capable of quickly learning near-optimal control law under the interference of random noise. In terms of tracking performance, the method reduces the tracking error by up to 98.7% compared with the traditional integral control while maintaining a stable control process. Therefore, this approach may provide an intelligent solution for control issues in AO systems.