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About

Yan Pan is a rising researcher in robotics and computational intelligence, whose work centers on advancing neural dynamics and model-free control for robotic systems. Pan’s primary contributions lie in developing inverse-free and pseudoinverse-free Zhang neurodynamics algorithms that solve time-variant nonlinear optimization problems—critical for real-time robot manipulator applications. Notably, Pan’s 2024 paper on inverse-free zeroing neural networks for time-variant nonlinear optimization has already garnered 23 citations, signaling strong early impact. In 2025, Pan extended this work to address path tracking control of robot manipulators with unknown models, introducing a novel discrete Jacobian-pseudoinverse-free estimator that eliminates the need for accurate system models—a major hurdle in practical robotics. This model-free, pseudoinverse-free approach, detailed in two subsequent papers (with 3 and 2 citations respectively), represents a significant departure from traditional methods, offering robust, computationally efficient solutions for real-world robotic arm control. Pan’s research is particularly notable for its focus on practical engineering challenges, making advanced control schemes accessible without requiring precise mathematical models. As a young investigator, Pan’s trajectory suggests a promising future in bridging theoretical neurodynamics with tangible robotic applications.