Haikun Wei
Papers
5
Total Citations
22
H-Index
3
About
Haikun Wei’s research bridges the frontiers of control theory and intelligent manufacturing, with key contributions in fault-tolerant control, underactuated system dynamics, and physics-informed machine learning. His work on terminal sliding mode control established a constructive framework for accommodating actuator faults in nonholonomic systems—a critical advance for autonomous vehicles and mobile robotics. Wei also rigorously analyzed the linear strong structural controllability of n-link inverted pendulums, providing foundational insights for underactuated mechanical systems widely studied in robotics and control communities. More recently, he has pioneered hybrid modeling approaches for manufacturing quality prediction. His physics-guided meta-learning method enables accurate surface roughness prediction under diverse working conditions with limited data, while his knowledge-based fuzzy broad learning system integrates theoretical models with error correction for grinding processes. These contributions directly address the industry challenge of balancing model accuracy with data efficiency. With his most cited works accumulating over 20 citations, Wei’s trajectory from robust control theory to data-efficient manufacturing intelligence exemplifies a seamless integration of mathematical rigor and practical engineering impact.
Research Focus
Key Achievements
Top Papers
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