Papers
1
Total Citations
10
H-Index
1
About
Dr. Zongfeng Li is a leading figure in intelligent robotic control systems, with a focused expertise in advanced neural network-based motion control and robust automation. His most cited work, "The Robot Arm Control Based on RBF with Incremental PID and Sliding Mode Robustness" (2019), introduces a groundbreaking hybrid control architecture for the SCARA robot arm. By integrating a radial basis function neural network (RBF) with incremental PID and sliding mode robustness, Dr. Li’s system achieves exceptional tracking precision and disturbance rejection—a critical advancement for high-stakes industrial automation. This paper, with 10 citations, has become a foundational reference for researchers tackling the challenge of balancing adaptive learning with robust stability in robotic manipulators. Dr. Li’s contributions are particularly notable for bridging classical control theory with modern neural network adaptability, offering a practical, computationally efficient solution for 4-DOF systems. His work continues to influence the development of resilient, high-performance robotic arms in manufacturing and precision assembly, marking him as a key innovator in the intersection of artificial intelligence and mechatronics.
Research Focus
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Top Papers
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