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About

Dr. Xingyong Song is a leading researcher in advanced control systems, robotics, and mechatronics education. His work centers on developing innovative control strategies for precision periodic tasks, a critical challenge in systems ranging from hard-drive read-write arms to robotic manipulators. His most cited paper, "Event-Triggered Modified Repetitive Control for Periodic Signal Tracking" (2018), introduces a groundbreaking approach that enhances the efficiency and robustness of repetitive control—a method proven effective for tracking periodic signals and rejecting disturbances. This contribution has garnered 15 citations, establishing a foundation for energy-aware precision control. Dr. Song is also a pioneer in educational robotics; he co-designed the SCUTTLE (Sensing, Connected Utility Transport Taxi for Level Environments) platform, an open-source, embedded Linux mobile robot used at Texas A&M University to bridge Mechatronics and IoT education. His earlier work on sensor fusion for SLAM (simultaneous localization and mapping) and 3D environment perception—combining stereo vision with laser structured light—has been cited for its practical impact on autonomous navigation. Through these contributions, Dr. Song has shaped both theoretical control design and hands-on engineering pedagogy, influencing how students and researchers approach periodic control and mobile robotics.