Anle Yang
Papers
2
Total Citations
273
H-Index
2
About
Anle Yang is a leading researcher in intelligent robotics and adaptive control systems, with a primary focus on enhancing the autonomy and precision of robotic manipulators and mobile robots. Their most significant contribution is the development of dynamic learning from adaptive neural control (ANC) with prescribed performance, a breakthrough that enables robot manipulators to achieve high-precision tracking even when faced with unknown system dynamics and external disturbances. This seminal work, published in 2017, has garnered 271 citations, underscoring its profound impact on the field of nonlinear control theory and robotic applications. Yang’s research elegantly integrates neural networks with backstepping control to compensate for modeling errors and disturbances, as demonstrated in their work on nonholonomic mobile robots. By introducing performance functions to guarantee transient and steady-state behavior, Yang has provided a robust framework for safe and reliable robot operation in uncertain environments. Their work is essential reading for researchers and students working at the intersection of adaptive control, neural networks, and autonomous robotics, offering practical solutions for next-generation intelligent systems.
Research Focus
Key Achievements
Top Papers
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