Min-Su Jo

Papers

Total Citations

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About

Min-Su Jo is a leading researcher in industrial robotics, specializing in precision control and vibration suppression for robotic manipulators. His work focuses on enhancing the performance of industrial robot arms through advanced iterative learning control (ILC) methods, addressing critical challenges in path accuracy and dynamic stability. Jo’s most-cited paper, "Improving Path Accuracy and Vibration Character of Industrial Robot Arms with Iterative Learning Control Method" (2024), has garnered 8 citations, reflecting its immediate relevance to manufacturing automation and robotics engineering. This contribution introduces novel ILC algorithms that iteratively refine robot trajectories, reducing positional errors and mitigating harmful vibrations during high-speed operations—a key advancement for applications in assembly, welding, and material handling. By bridging theoretical control theory with practical robotic systems, Jo’s work enables more precise and reliable automation, directly impacting productivity and quality in smart factories. His research is particularly notable for its focus on real-time implementation, offering scalable solutions for existing industrial robots without requiring hardware modifications. As a rising scholar, Min-Su Jo continues to push the boundaries of motion control, making him a valuable resource for students and engineers seeking to understand cutting-edge techniques in robotic precision and vibration management.