Yan‐Jun Liu
Papers
8
Total Citations
217
H-Index
5
About
Yan-Jun Liu is a prolific researcher whose work sits at the dynamic intersection of adaptive control theory, reinforcement learning, and autonomous robotic systems. His most significant contributions center on developing intelligent control algorithms for nonlinear and time-delayed systems, with particular emphasis on wheeled mobile robots operating under real-world uncertainties and constraints. Liu's foundational work has advanced adaptive dynamic programming (ADP) and reinforcement learning frameworks to address challenging tracking control problems in partially uncertain, time-delayed nonlinear systems. His 2019 paper on ADP-based online tracking control has garnered 77 citations, reflecting the community's strong reception of his Hamilton-Jacobi-Bellman-based approaches. Complementing this, his adaptive neural network controller for state-constrained robotic systems (62 citations, 2017) introduced innovative nonlinear mapping techniques to handle time-varying constraints in multi-joint robots — a practically important advancement over classical constant-constraint methods. More recently, Liu has extended his research into event-triggered control strategies, distributed multi-robot formation problems, and soft terrain environments, demonstrating a broadening scope that addresses emerging challenges in field robotics. His later work on flapping-wing robotic aircraft further illustrates his willingness to tackle complex, unconventional platforms. Across his body of work, Liu has established himself as a meaningful contributor to intelligent, learning-based control systems for next-generation autonomous robots.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8