Qing Yang

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About

Qing Yang is a control systems researcher whose work centers on advanced motion control, robotics, and intelligent control theory, with a particular focus on permanent magnet synchronous motor (PMSM)-driven robotic systems. Yang's research addresses fundamental challenges in position servo control, including modeling errors, external disturbances, and unknown load dynamics that compromise system accuracy and stability in real-world applications. Among Yang's most recognized contributions is the development of neural network-based dynamic surface control strategies for multi-joint robotic manipulators. By integrating radial basis function (RBF) neural networks with dynamic surface control frameworks, Yang's 2022 work — which has accumulated 24 citations — introduced unknown load observers to significantly enhance both precision and robustness in PMSM-driven systems. Building on this foundation, Yang's 2023 research combined dynamic surface integral methods with nonsingular fast terminal sliding mode control, achieving improved disturbance rejection for robotic manipulators and garnering 16 citations within a short timeframe. Yang's contributions are particularly valuable for researchers and engineers working on intelligent robotic systems, offering practical, mathematically rigorous solutions to longstanding challenges in nonlinear control and autonomous manipulation.