Chenxi Sun

Chinese Academy of Sciences, Shandong Institute of Automation

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About

Chenxi Sun is a pioneering researcher in intelligent robotics and autonomous vehicle control systems, whose work bridges classical control theory with modern machine learning approaches. His most influential contribution, "Balance control of two-wheeled self-balancing robot based on Linear Quadratic Regulator and Neural Network" (2013, 37 citations), introduced a groundbreaking hybrid control method that combines LQR optimization with neural network adaptability. This approach effectively addresses the fundamental challenge of stabilizing inherently unstable, nonlinear, and strongly coupled systems—a problem central to robotics and autonomous platforms. Sun's work demonstrates how neural networks can enhance traditional linear controllers, enabling more robust real-time balance in dynamic environments. His additional research on vision-based guidance for autonomous guided vehicles (2012, 10 citations) further showcases his expertise in perception-driven navigation, where camera systems extract road parameters to direct vehicle movement without physical tracks. Together, these contributions have advanced the practical deployment of self-balancing robots and vision-guided AGVs in industrial automation. Sun's innovative fusion of control theory and neural computation continues to influence researchers developing next-generation autonomous systems that must operate reliably in unpredictable, real-world conditions.