Hanyi Huang
Papers
2
Total Citations
4
H-Index
2
About
Hanyi Huang is a rising researcher in the field of intelligent robotics and control systems, with a primary focus on bipedal locomotion and trajectory tracking. Their work centers on developing novel hybrid control architectures that integrate adaptive neuro-fuzzy inference systems (ANFIS), reinforcement learning (RL), and active force control (AFC) to enhance the stability and precision of biped robots. Huang’s major contributions include pioneering the AFC-ANFIS model, which leverages iterative learning to dynamically improve tracking performance, and the AFC-RL controller, which combines the robustness of reinforcement learning with the simplicity of active force control. These innovations address critical challenges in real-time adaptive control for multi-link robotic systems, such as the five-link biped robot. Although early in their career, Huang’s publications have already garnered citations, signaling growing interest in their methodologies. Their work stands out for its practical integration of machine learning with classical control theory, offering scalable solutions for humanoid robotics. Huang’s research holds promise for advancing autonomous systems in rehabilitation, assistive technology, and industrial automation.
Research Focus
Key Achievements
Top Papers
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