Papers
13
Total Citations
601
H-Index
10
About
Han-Xiong Li is a leading figure in intelligent control and robotics, renowned for pioneering the integration of fuzzy logic, neural networks, and reinforcement learning. His most influential contribution is the development of the probabilistic fuzzy logic system (PFLS), a groundbreaking framework that marries probabilistic reasoning with fuzzy sets for robust modeling and control—a work that has garnered 193 citations. Li has also made seminal advances in adaptive control for robotic manipulators, notably through neuro-fuzzy dynamic-inversion-based strategies (78 citations) and fuzzy adaptive variable structure controllers (75 citations), which provide stability guarantees without requiring explicit system models. His research extends to autonomous navigation, where he introduced hybrid hierarchical Q-learning algorithms (70 citations) that enable mobile robots to operate effectively in dynamic environments. More recently, Li has pushed the boundaries of machine learning with incremental reinforcement learning using prioritized sweeping (70 citations), allowing agents to adapt to changing reward structures. With a career spanning over two decades, his work consistently bridges theoretical rigor and practical application, making him a highly cited authority in intelligent systems and robotic control.
Research Focus
Key Achievements
Top Papers
- 1A probabilistic fuzzy logic system for modeling and control193 citations · 2005
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- 5Hybrid Control for Robot Navigation - A Hierarchical Q-Learning Algorithm70 citations · 2008
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- 7Hybrid MDP based integrated hierarchical Q-learning20 citations · 2011
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- 9Fuzzy avoidance control strategy for redundant manipulators15 citations · 1999
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