Jun Moon
Papers
2
Total Citations
15
H-Index
2
About
Jun Moon is a leading researcher in advanced robotics control and intelligent systems, with a primary focus on sliding-mode control theory and neural network-based inverse kinematics. His major contributions include the development of finite-time continuous nonsingular terminal modified adaptive-gain super-twisting control (FT-CNT-MAG-STC), a groundbreaking framework that achieves fast finite-time convergence and robust performance for second-order disturbed systems, as demonstrated in his highly cited 2022 work (13 citations) applied to a 2-DOF planar robot manipulator. This work significantly improves upon existing sliding-mode controllers by eliminating singularities and ensuring continuous control inputs. More recently, Moon has pioneered the EMIKNet (Expanding Multiple-Instance Inverse Kinematics Network), a novel neural network approach that efficiently solves inverse kinematics for multiple end-effectors and multiple solutions, addressing critical computational bottlenecks in real-time robot control. His research has garnered attention for its practical impact on robotic manipulation, with applications ranging from industrial automation to advanced robotic systems. Moon’s work bridges theoretical rigor with real-world applicability, making him a notable figure in control engineering and robotics.
Research Focus
Key Achievements
Top Papers
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- 2