Kaiming Yang
Papers
3
Total Citations
12
H-Index
3
About
Kaiming Yang is a researcher specializing in advanced motion control systems, robotics, and intelligent automation. His work focuses on overcoming critical challenges in precision motion control, particularly for flexible joint robots and ultra-precision manufacturing equipment. Yang’s major contributions include developing a self-adaptive composite control scheme for flexible joint robots using RBF neural networks, which eliminates the need for difficult-to-measure acceleration and jerk signals while mitigating noise interference. He also pioneered a cascaded iterative learning control (ILC) scheme for dual-stage actuated wafer stages, achieving ultra-precision motion performance essential for semiconductor manufacturing. Additionally, Yang has advanced mobile robot navigation in unknown environments through T–S neuro-fuzzy systems, enabling adaptive decision-making without prior maps. His most cited work, with 5 citations, addresses a fundamental limitation in flexible joint robot control, while his wafer stage ILC scheme (4 citations) and neuro-fuzzy navigation approach (3 citations) demonstrate sustained impact in precision engineering and autonomous robotics. Yang’s research bridges theoretical control design with practical implementation, offering solutions that enhance accuracy, robustness, and adaptability in real-world mechatronic systems—critical for next-generation manufacturing and service robotics.
Research Focus
Key Achievements
Top Papers
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