Qiyan Yan

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About

Qiyan Yan’s research focuses on advancing robotic manipulation through intelligent control systems, with a particular emphasis on neural network-based solutions for trajectory tracking, inverse kinematics, and motion planning. Their most cited work, “Research on Manipulator Tracking Control Algorithm Based on RBF Neural Network” (2021, 14 citations), leverages the self-learning and nonlinear mapping capabilities of Radial Basis Function (RBF) networks to address the strong coupling and high nonlinearity inherent in manipulator control. This contribution has been foundational for researchers seeking robust, adaptive control methods in robotics. Yan further extended this approach in “Research on Inverse Kinematics of Manipulator Based on Neural Network” (2022), offering an efficient alternative to traditional inverse kinematics solutions, and in “Robot trajectory planning based on GA-RBF neural network” (2025), which combines RBF networks with genetic algorithms to enhance trajectory accuracy and smoothness in uncertain environments. By integrating neural networks with classical robotics challenges, Yan’s work provides practical, data-driven frameworks that improve manipulator performance, making it valuable for students and researchers in robotics, control theory, and artificial intelligence.