Papers
11
Total Citations
215
H-Index
7
About
Linyan Han is a leading researcher in robotics and control systems, specializing in advanced motion planning, force estimation, and autonomous navigation for robot manipulators and humanoid robots. Their major contributions include developing high-order finite-time observers for sensorless interaction force estimation, achieving high precision without force sensors—a breakthrough cited 72 times. Han also pioneered real-time dynamic obstacle avoidance using cascaded nonlinear model predictive control (MPC) with artificial potential fields, enabling safe, low-latency responses to moving obstacles (55 citations). Their work on robust bipedal locomotion integrates disturbance observer-based cascaded MPC, exploiting multiple balance strategies like ankle, stepping, and hip adjustments (23 citations). Additionally, Han has advanced visual servoing with fuzzy adaptive MPC and dual-rate prescribed performance control, addressing kinematic and view constraints. Notable achievements include TinyML-based feature detection for miniature robots and the Mega-Joey platform for autonomous sewer inspection. With over 200 total citations, Han’s research bridges theory and practice, offering scalable solutions for industrial, service, and infrastructure robotics.
Research Focus
Key Achievements
Top Papers
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- 9TinyML-Based In-Pipe Feature Detection for Miniature Robots4 citations · 2025
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