Papers
7
Total Citations
75
H-Index
5
About
Tian Shi is a leading researcher in intelligent robotics and human-robot interaction, with a primary focus on rehabilitation and collaborative robots. His work centers on developing advanced control systems that enable robots to safely and effectively interact with humans, particularly in medical and industrial settings. A major contribution is his noise-tolerant neural algorithm for solving time-varying matrix inverse problems, which has garnered 32 citations and offers a control-theoretic approach to real-time computation. Shi has pioneered the use of surface electromyography (sEMG) signals for intention recognition in rehabilitation robots, as seen in his 2019 RBF neural network approach (14 citations) that estimates joint movements from muscle signals. He has also developed innovative model predictive control methods for lower limb rehabilitation robots, with a projected active set conjugate gradient approach (9 citations) that addresses both passive and active rehabilitation. His recent work on composite learning control for high-degree-of-freedom robot manipulators (5 citations) and reinforcement learning-based impedance learning for industrial assembly (4 citations) demonstrates his versatility in tackling complex control challenges. Shi’s research is notable for its practical applications in stroke rehabilitation and manufacturing, bridging theoretical control methods with real-world robotic systems.
Research Focus
Key Achievements
Top Papers
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