Laser Beam Pointing Stabilization Control through Disturbance Classification

Area of Science:

• Optics and Photonics
• Control Systems Engineering
• Machine Learning Applications

Background:

• Laser beam pointing instability is a significant issue in various applications due to diverse environmental and mechanical disturbances.
• Existing control strategies face challenges in adapting to the varied nature of these disturbances.

Purpose of the Study:

• To develop a robust laser beam pointing control system capable of stabilizing both position and angle.
• To enhance control system adaptability to a wide range of disturbances using a data-driven approach.

Main Methods:

• Utilized Fast Steering Mirrors (FSMs) and Position Sensitive Devices (PSDs) for beam stabilization.
• Analyzed the kinematic model using ABCD matrix for a four degree-of-freedom (DOF) beam vector.
• Designed a Jacobian matrix feedback controller for precise calibration.
• Implemented a data-driven disturbance classification method using a Recurrent Neural Network (RNN).

Main Results:

• The proposed system effectively stabilizes laser beam position and angle.
• The RNN model accurately classifies disturbance types in real time.
• Adaptive control parameter selection based on classified disturbances improved system performance.
• Experimental verification demonstrated the system's effectiveness under various external disturbances.

Conclusions:

• The developed laser beam pointing control system offers enhanced stability and adaptability.
• The integration of RNN-based disturbance classification enables universal applicability across different disturbance scenarios.
• This approach represents a significant advancement in maintaining laser beam precision in dynamic environments.