Heqing Sun

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About

Heqing Sun’s research sits at the intersection of advanced control theory and industrial robotics, with a focus on enhancing precision, reliability, and autonomy in automated systems. His work on iterative learning control (ILC) is exemplified by his 2014 study on a cross-coupled, non-lifted norm optimal ILC approach, applied to a multi-axis robotic testbed—a contribution that has garnered 9 citations for its novel handling of multi-axis coordination. More recently, Sun has turned his attention to the critical area of robot health monitoring, particularly in strain wave gears, which are essential components in industrial robots. His 2019 paper on this topic, with 8 citations, addresses the pressing need for early fault detection to prevent costly downtime and quality losses in manufacturing. By developing methods to diagnose degradation before failure occurs, Sun’s work directly supports the shift toward predictive maintenance in smart factories. His contributions are notable for bridging theoretical control design with practical, industry-relevant applications, making his research valuable for both scholars and engineers seeking to improve robot performance and longevity.