Xiangxiang Meng
Papers
2
Total Citations
40
H-Index
2
About
Xiangxiang Meng is a leading researcher in advanced robotics control, specializing in neural network-driven motion systems for permanent magnet synchronous motor (PMSM)-actuated manipulators. Her work addresses critical challenges in industrial robotics—specifically, the degradation of positioning accuracy and stability caused by modeling errors, external disturbances, and unknown loads. Meng’s most cited paper (2022, 24 citations) introduces a novel RBF neural network dynamic surface position controller with an unknown load observer for n-joint robots, significantly enhancing servo precision. Building on this, her 2023 study (16 citations) develops a dynamic surface integral nonsingular fast terminal sliding mode control framework that integrates disturbance rejection, achieving robust, high-speed trajectory tracking even under severe uncertainties. These contributions are pivotal for next-generation manufacturing and collaborative robotics, where precise, disturbance-tolerant motion is essential. Meng’s work is widely cited by control engineers and roboticists, reflecting its practical impact on real-time adaptive control systems. Her innovative fusion of neural networks with sliding mode techniques marks a significant step toward intelligent, self-correcting robotic platforms.
Research Focus
Key Achievements
Top Papers
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