Yuliang Shang

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About

Yuliang Shang is a researcher specializing in advanced control systems for robotic and electromechanical applications, with a primary focus on neural network-based dynamic control, permanent magnet synchronous motor (PMSM) servo systems, and intelligent load observation. Their most cited work, "Neural network dynamic surface position control of n‐joint robot driven by PMSM with unknown load observer" (2022, 24 citations), addresses critical challenges in robotic position servo control—specifically, low accuracy and poor stability caused by modeling errors, external disturbances, and unknown loads. By integrating radial basis function (RBF) neural networks with dynamic surface control and load observers, Shang’s research offers a robust solution that enhances precision and stability in multi-joint robotic systems. This contribution is particularly impactful for industrial automation and advanced robotics, where reliable motor control under uncertain conditions is essential. Shang’s work bridges theoretical control methods with practical engineering challenges, demonstrating a clear ability to innovate in nonlinear system control. With growing recognition in the field, their research continues to influence developments in intelligent servo control and adaptive robotics.