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About

Changlin Song is a researcher at the forefront of intelligent robotics and control systems, with key research areas spanning industrial robot servo control, rehabilitation exoskeletons, and robotic vision. Song’s major contributions include developing a hybrid sparrow search algorithm (HSSA) for PID parameter optimization, which significantly enhances the stability of industrial robot servo systems by mitigating jitter caused by joint friction and load changes. In rehabilitation robotics, Song proposed a nonlinear-observer-based neural fault-tolerant control method for exoskeleton joints with electro-hydraulic actuators, addressing critical challenges like modeling uncertainties and actuator faults to improve safety for stroke patients and the elderly. Additionally, Song introduced YOLO-ARM, an enhanced YOLOv7 framework with an adaptive attention receptive module, achieving high-precision object detection for robotic vision in adverse conditions. With over 25 citations across these pioneering works, Song’s research has garnered attention for its practical impact on industrial automation and assistive technology. Notably, the HSSA-based PID control study (2025) has already accumulated 12 citations, underscoring its relevance in advancing robotic stability and precision.