Tae-Hee An

Papers

Total Citations

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About

Tae-Hee An’s research focuses on intelligent control systems for robotics, particularly neural-network-based approaches for enhancing stability and dexterity in dynamic environments. His work spans three key areas: robotic hand manipulation, robust industrial manipulator control, and balancing control for mobile robots. An’s most notable contributions include developing a flexible three-fingered robotic hand for unstructured environments, designing robust neural network control schemes to compensate for parametric uncertainties in industrial robot systems, and creating a neural-network-tuned PID controller for two-wheeled inverted pendulum mobile robots—achieving more stable balancing than conventional PID methods. Although his published works have accumulated modest citation counts (each with 2 citations), they represent foundational efforts in applying neural networks to real-world robotic control challenges. An’s research demonstrates practical engineering solutions, such as using neural networks to adaptively tune control gains based on varying payloads (e.g., user weight in mobile robots), bridging the gap between theoretical control theory and industrial application. His work contributes to the growing field of intelligent robotics, particularly in making robots more adaptable and reliable in unstructured, human-centered environments.