Papers
4
Total Citations
68
H-Index
4
About
Minhao Liu is a robotics researcher whose work centers on intelligent control systems and autonomous navigation for mobile robots. His primary research areas include adaptive control, sliding mode control, and reinforcement learning, with a focus on enhancing the stability, trajectory tracking, and obstacle avoidance capabilities of wheeled robots. Liu’s major contributions include the development of an adaptive sliding mode attitude controller integrated with a learning-based Radial Basis Function Neural Network (RBFNN) for two-wheel mobile robots, which has garnered 37 citations, highlighting its impact on improving robot balance and robustness. He also proposed a RISE-based asymptotic prescribed performance controller for two-wheeled self-balancing robots (13 citations) and an improved Q-Learning algorithm optimized with a flower pollination approach for unmanned ground robot path planning (11 citations), addressing slow convergence and collision risks in complex environments. His comparative study on adaptive trajectory tracking for four-wheel mobile robots (7 citations) further demonstrates his expertise in prescribed-performance control. Liu’s work is notable for its practical applications in autonomous systems, offering innovative solutions to real-world challenges in robot mobility and safety.
Research Focus
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