Papers
5
Total Citations
94
H-Index
3
About
Dr. Yuanji Liu is a rising leader in the field of advanced robotics and nonlinear control systems, with a primary focus on the trajectory tracking and gait optimization of complex mechanical systems. His most impactful work centers on developing robust, finite-time control strategies for uncertain mechanical systems, as demonstrated by his highly cited 2019 paper on extended state observer-augmented control (66 citations). Dr. Liu has made significant contributions to adaptive sliding mode control, proposing fixed-time convergence methods that ensure stability and precision even under system uncertainties. His recent research pushes the boundaries of humanoid robotics, introducing innovative frameworks that combine Zeroing Neural Networks (ZNN) with the Angular Momentum Linear Inverted Pendulum (ALIP) model for real-time gait optimization. He has also developed a hierarchical optimization structure that integrates Model Predictive Control with whole-body planning, enabling stable walking in underactuated humanoid robots lacking ankle roll degrees of freedom. Through his work, Dr. Liu bridges theoretical control advances with practical robotic applications, establishing himself as a key figure in the next generation of intelligent, adaptive robotic systems.
Research Focus
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