Papers
72
Total Citations
1,003
H-Index
16
About
Tie Zhang is a prominent robotics and control systems researcher whose work centers on advanced motion control, robotic manipulation, and intelligent autonomous systems. His most significant contributions lie in developing finite-time control frameworks for robotic manipulators under uncertainty—a body of work that has collectively garnered hundreds of citations. His 2016 paper on finite-time H∞ control for high-precision tracking (87 citations) stands as a landmark contribution, offering a practical solution that bypasses the computationally demanding Hamilton-Jacobi and Riccati equations. Complementing this, his neural network-based and adaptive finite-time control approaches from 2012 and 2013 demonstrated early mastery in merging intelligent learning with rigorous stability guarantees. Zhang has also made meaningful strides in robotic grinding and surface processing, proposing reinforcement learning and sliding-mode iterative methods that enhance force control precision in industrial applications. His 2020 work on time-optimal trajectory planning reflects a continued evolution toward real-world deployment efficiency. With contributions spanning indoor navigation, fuzzy control, and output-feedback nonlinear systems, Zhang's research portfolio represents a cohesive and impactful effort to make robotic systems smarter, faster, and more reliable in dynamic environments.
Research Focus
Key Achievements
Top Papers
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- 4Time-optimal and Smooth Trajectory Planning for Robot Manipulators71 citations · 2020
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