Papers
29
Total Citations
583
H-Index
9
About
Young Hoon Joo is a prolific researcher whose work spans intelligent control systems, fuzzy logic, robotics, and multi-agent systems. With a career stretching from the mid-1990s to the present, Joo has made foundational contributions to fuzzy system modeling, most notably through his early work on hybrid genetic algorithm schemes for fuzzy partitioning (1997), which laid groundwork for adaptive intelligent systems. His research gained significant momentum with decision-making methodologies under uncertainty, exemplified by his highly cited 2018 paper on interval-valued intuitionistic hesitant fuzzy entropy for industrial robot selection, which has accumulated 172 citations and remains a benchmark in multi-criteria decision analysis. Joo's contributions extend into robust control theory, particularly T-S fuzzy systems and event-triggered control frameworks, alongside advanced robotics applications including dual-arm robot control using sliding mode and neural network approaches. His more recent work addresses swarm robotics, formation control of nonholonomic mobile robots, and visual object tracking for autonomous systems, reflecting a forward-looking research trajectory aligned with AI-driven robotics. Collectively accumulating over 500 citations, Joo's interdisciplinary portfolio bridges theoretical control engineering with practical intelligent systems, making his work essential reading for researchers in autonomous robotics and computational intelligence.
Research Focus
Key Achievements
Top Papers
- 1
- 2Fuzzy system modeling by fuzzy partition and GA hybrid schemes74 citations · 1997
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- 5Adaptive neural network second-order sliding mode control of dual arm robots48 citations · 2017
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- 8Learning from adaptive control under relaxed excitation conditions21 citations · 2019
- 9Parameter estimator integrated-sliding mode control of dual arm robots19 citations · 2017
- 10