Chang Don Lee

Papers

Total Citations

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About

Chang Don Lee is a robotics and control systems researcher whose work sits at the intersection of artificial intelligence and mechanical engineering. His most recognized contribution focuses on the development of intelligent control strategies for robot manipulators, particularly through the application of sliding mode impedance control (SMIC) combined with real-time artificial intelligence algorithms. In his notable 2010 paper, Lee advanced the field by integrating radial basis function neural networks (RBFNNs) into impedance control frameworks, enabling more adaptive and precise end-effector tracking for robotic systems. This approach addressed a longstanding challenge in robotics: dynamically estimating design parameters in real time, making robot interactions with environments more robust and responsive. While still building its citation record, this work represents a meaningful contribution to the growing body of research that bridges classical control theory with modern machine learning techniques. Lee's research holds particular relevance for students and engineers working on human-robot interaction, force control, and intelligent automation, areas that continue to grow in importance across manufacturing, healthcare, and service robotics sectors.