Papers
7
Total Citations
274
H-Index
6
About
Mingxuan Sun is a leading researcher in the field of advanced control theory, with a primary focus on iterative learning control (ILC), repetitive learning control, and robotic manipulator trajectory tracking. Sun’s most impactful contribution is the development of adaptive repetitive learning control methods that relax the strict requirement for initial repositioning in robotic systems—a breakthrough that significantly enhances practical applicability. Their seminal 2006 paper on this topic has garnered 175 citations, underscoring its influence in the field. Sun has also advanced ILC for nonlinear systems with higher relative degree, proposing novel updating laws that reduce the need for high-order error differentiations. More recently, Sun has explored zeroing neural networks (ZNN) for time-variant optimization, introducing finitely-activated models with exact settling times. With a career spanning over two decades, Sun’s work bridges theoretical rigor and real-world implementation, offering robust solutions for uncertain, time-varying systems. Their research continues to shape modern control engineering, particularly in robotics and adaptive systems.
Research Focus
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Top Papers
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