Feng Chang-yong

Papers

Total Citations

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About

Feng Chang-yong is a researcher focused on advancing intelligent automation and control systems, with particular expertise in deep learning, machine vision, and robotic manipulation for industrial applications. His most cited work, "Optimization of Sorting Robot Control System Based on Deep Learning and Machine Vision" (2022, 15 citations), addresses a critical challenge in coal processing: enhancing the control technology of coal gangue dry separation to replace manual sorting in washing plants. In this study, he systematically compared traditional PID control with dynamic domain fuzzy self-tuning PID, demonstrating how machine vision and deep learning can determine the ideal position and orientation for robotic grasping. This contribution directly improves sorting accuracy and operational efficiency in resource recovery. Feng’s research bridges theoretical control methods and practical industrial robotics, offering scalable solutions for automation in harsh environments. His work has been cited in subsequent studies on intelligent sorting and adaptive control, reflecting its relevance to both academic and engineering communities. By integrating real-time visual feedback with advanced control strategies, Feng Chang-yong continues to push the boundaries of smart manufacturing and sustainable resource processing.