Papers
16
Total Citations
159
H-Index
7
About
Lijun Zhu is a leading researcher in multi-robot systems and legged locomotion, with a focus on quadruped and bipedal robots. His work centers on developing robust, optimization-based control frameworks that address critical challenges in calibration, gait synchronization, and navigation under uncertainty. Zhu’s major contributions include a novel dual-robot calibration method using convex optimization and Lie derivatives, which decouples intercoupling transformations in multi-robot systems, and a robust convex model predictive control (MPC) approach for quadruped locomotion under uncertain friction and dynamics. His papers have garnered significant attention, with top works accumulating 38 and 36 citations respectively, reflecting their impact on the field. Notably, Zhu has pioneered hierarchical learning frameworks for efficient multi-modal locomotion, distributed MPC for formation control with gait synchronization, and prescribed-time synchronization for networked Euler-Lagrange systems. His research also explores natural oscillation patterns in locomotion, inspired by animal dynamics, and agile trajectory planning that accounts for motion anisotropy. With a career spanning from foundational neural control studies to cutting-edge robust optimization, Zhu’s work is essential reading for students and researchers advancing autonomous, multi-agent robotic systems.
Research Focus
Key Achievements
Top Papers
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- 7Stable Locomotion of Biped Robot With Gaits of Sinusoidal Harmonics7 citations · 2023
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- 10Neural control for coordinated natural oscillation patterns3 citations · 2013