Papers
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About
Zunjie Yu is a researcher in advanced control systems, specializing in the intersection of fractional-order calculus, neural networks, and robotic dynamics. His work addresses critical challenges in robot trajectory tracking, particularly the need for higher accuracy and faster convergence under uncertain conditions and external disturbances. Yu's most impactful contribution is the development of an adaptive fractional-order fast terminal sliding mode controller, which integrates radial basis function (RBF) neural networks to intelligently compensate for system uncertainties. This innovative approach, detailed in his 2020 paper "Fractional-Order Nonsingular Terminal Sliding Mode Control of Uncertain Robot Neural Network," has garnered attention for its potential to enhance the robustness and precision of robotic manipulators. By combining fractional-order dynamics with neural network adaptation, Yu offers a novel solution to the persistent problem of chattering and slow response in traditional sliding mode control. His work represents a meaningful step toward more intelligent, resilient robotic systems, making him a notable contributor to the fields of nonlinear control and robotics.
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