Papers
7
Total Citations
32
H-Index
3
About
Tan Zhang is a rising researcher in the field of robotic control systems, with a primary focus on advanced trajectory tracking and motion control for robotic manipulators and mobile robots. Their work centers on developing innovative control strategies that address critical challenges such as system uncertainties, external disturbances, and position constraints. Zhang’s major contributions include pioneering the use of adaptive neural network control schemes integrated with disturbance observers, introducing time-synchronized convergence control to ensure simultaneous component convergence, and developing novel barrier functions—including the first-ever time-variant asymmetric integral barrier function—to handle state constraints in nonlinear systems. Their research has garnered significant attention, with their most-cited paper on adaptive neural network tracking control accumulating 14 citations since 2024. Zhang has also explored bio-inspired robotics, designing a centipede-inspired robot with passive terrain adaptation for improved mobility on unstructured terrain. With a growing portfolio of publications in 2023-2025, Zhang is establishing themselves as an innovator in constrained robot control and disturbance rejection, making their work highly relevant for students and researchers interested in advanced robotic manipulation and nonlinear control theory.
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