Papers
33
Total Citations
1,053
H-Index
13
About
Lixian Zhang is a prominent researcher whose work spans control systems theory, reinforcement learning, and autonomous robotics—fields where rigorous mathematical foundations meet real-world engineering challenges. His contributions have significantly advanced our understanding of stochastic switching systems, particularly Markov and semi-Markov jump linear systems, where he has tackled the difficult problem of incomplete or uncertain transition information, earning over 118 citations for his foundational work in this area. Zhang has also made substantial strides in fuzzy systems control, developing robust frameworks for Takagi-Sugeno fuzzy systems operating under unreliable communication conditions—work that has accumulated nearly 150 citations. Beyond classical control theory, Zhang has pioneered stability-guaranteed reinforcement learning approaches for robotic control, addressing the critical gap between data-driven learning and provable system stability, with his 2020 actor-critic paper already amassing 145 citations. His applied robotics research is equally impressive, encompassing model predictive control for mobile robots and autonomous navigation frameworks for innovative terrestrial-aerial bimodal vehicles. This breadth—from theoretical stochastic analysis to cutting-edge autonomous systems—reflects a researcher who consistently bridges abstract mathematical rigor with practical robotic applications, making his work essential reading for both control theorists and robotics engineers alike.
Research Focus
Key Achievements
Top Papers
- 1Actor-Critic Reinforcement Learning for Control With Stability Guarantee145 citations · 2020
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- 7Autonomous and Adaptive Navigation for Terrestrial-Aerial Bimodal Vehicles66 citations · 2022
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