Ultrafast lasers, characterized by femtosecond and picosecond pulse durations, have revolutionized material processing due to their high energy density and minimal thermal diffusion, and have played a transformative role in precision manufacturing. This review first traces the progression from early ruby lasers to modern titanium–sapphire lasers, highlighting breakthroughs like Kerr-lens mode-locking and chirped pulse amplification. It also examines the interaction mechanisms between ultrafast pulses and various materials, including metals, dielectrics, and semiconductors. Applications of ultrafast lasers in microstructure processing techniques are detailed, such as drilling, cutting, surface ablation, and nano welding, demonstrating the versatility and precision of the technology. Additionally, it covers femtosecond laser direct writing for optical waveguides and the significant advancements in imaging and precision measurement. This review concludes by discussing potential future advancements and industrial applications of ultrafast lasers.

External vibration, shock, unbalanced torque and other uncertain disturbances are mainly transmitted to the motor rotor through the bearing friction. To restrain the uncertain friction disturbances and improve the speed stability of a permanent magnet synchronous motor (PMSM), an optimized active disturbance rejection control (ADRC) algorithm is proposed in this study. Firstly, an auxiliary model of friction and a reduced-order processing method are introduced into extended state observation (ESO) to reduce the burden of single ESO and promote the compensation accuracy of disturbances. In addition, a supervisory radial basis function (SRBF) is employed to supervise and promote the error elimination efficiency of the nonlinear state error feedback rate (NLSEF). The hybrid control algorithm makes up for the deficiency of typical ADRC through the fusion of multiple control quantities. Simulation and experimental results show that the proposed algorithm has strong anti-disturbance performance and effectively solves the problem of low-speed crawling.

Pose measurement of non-cooperative targets in space is one of the key technologies in space missions. However, most existing methods simulate well-lit environments and do not consider the degradation of algorithms in low-light conditions. Additionally, due to the limited computing capabilities of space platforms, there is a higher demand for real-time processing of algorithms. This paper proposes a real-time pose measurement method based on binocular vision that is suitable for low-light environments. Firstly, the traditional point feature extraction algorithm is adaptively improved based on lighting conditions, greatly reducing the impact of lighting on the effectiveness of feature point extraction. By combining point feature matching with epipolar constraints, the matching range of feature points is narrowed down to the epipolar line, significantly improving the matching speed and accuracy. Secondly, utilizing the structural information of the spacecraft, line features are introduced and processed in parallel with point features, greatly enhancing the accuracy of pose measurement results. Finally, an adaptive weighted multi-feature pose fusion method based on lighting conditions is introduced to obtain the optimal pose estimation results. Simulation and physical experiment results demonstrate that this method can obtain high-precision target pose information in a real-time and stable manner, both in well-lit and low-light environments.

Short-wave infrared star sensors have become a key technology for all-time attitude determination within the atmosphere, in which the star identification algorithm plays a fundamental role. However, due to the limited number of detectable stars in infrared images, achieving robust and accurate identification remains challenging. To address this issue, this paper proposes a star identification algorithm based on ordered angular distance verification. The algorithm first extracts radial and adjacency features via full-field-of-view sorting to mitigate the impact of “edge loss”. It then employs a fast initial matching that combines hash table lookup with binary search, substantially reducing the number of candidate navigation stars requiring detailed matching. Subsequently, a local search matching procedure corrects index misalignment caused by false or missing stars, while angular distance invariance verification prevents false matches; the combination of these mechanisms significantly enhances the algorithm’s robustness. In simulations using 5000 star images, the proposed algorithm achieves an identification rate of 99.48%. It maintains a rate above 96% under position noise, magnitude noise, and false stars. The average processing time per star image is 10.57 ms, approximately 39% of that required by the conventional grid algorithm (27.01 ms). The simulation results demonstrate that the proposed algorithm achieves high identification accuracy and maintains strong robustness in complex noise environments.