Prediction and control of pointing accuracy in precision opto-mechanical systems with uncertainty reduction

Pointing accuracy is a core performance metric for opto-mechanical systems, yet its stability is often compromised by multi-source uncertainties during assembly. This paper proposes a novel prediction and control method with uncertainty reduction to enhance alignment accuracy and efficiency. The approach includes: (1) identifying uncertainty sources through system architecture analysis and quantifying them via multivariate distribution feature extraction and subinterval sampling; (2) establishing a kinematic model using the D-H method and building a pointing accuracy prediction model that integrates Nataf decomposition and Polynomial Chaos Expansion to determine the theoretical system accuracy limit; (3) introducing an alignment control strategy to reduce preload uncertainty, enabling performance-driven inverse determination and adaptive regulation. Alignment experimental validation shows that the pointing accuracy was improved from 329.61 arc-seconds to 195.05 arc-seconds (40.82 % enhancement), approaching the theoretical limit of 177.48 arc-seconds. The approach significantly improves alignment reliability and efficiency, offering theoretical support for uncertainty-reduced prediction and control in opto-mechanical systems. • Accurate prediction and control of pointing accuracy in opto-mechanical systems is fundamental to improving their assembly performance. • A pointing accuracy prediction model incorporating multi-source uncertainties and assembly deformations was established. • Compared to existing methods, the new prediction model reduces preload uncertainty to achieve higher assembly accuracy. • The model provides a theoretical foundation for enhancing assembly efficiency and quality.