Papers
3
Total Citations
216
H-Index
3
About
Yizhuo Sun is a leading researcher in advanced robotic control systems, with a primary focus on trajectory tracking, neural network-based adaptive control, and sliding-mode control for uncertain robotic systems. Their major contributions lie in developing innovative control strategies that address critical challenges in robotics, including system uncertainties, actuator faults, and the singularity problem in terminal sliding-mode control. Sun’s most impactful work, “Neural Network-Based Tracking Control of Uncertain Robotic Systems: Predefined-Time Nonsingular Terminal Sliding-Mode Approach” (2022), has garnered 118 citations, demonstrating its significance in the field. This study introduced a radial basis function neural network (RBFNN) to estimate uncertainties while avoiding the singularity issue inherent in traditional sliding-mode control. Complementing this, their fixed-time control approach (2022, 76 citations) further advanced adaptive neural network techniques for robotic trajectory tracking. Most recently, Sun’s 2025 work on predefined-time reliable control with prescribed performance (22 citations) addresses actuator faults and parametric uncertainties, showcasing their ongoing commitment to robust, high-performance robotic systems. Through these achievements, Sun has established themselves as a key innovator in predefined-time and fixed-time control theory, with direct applications to next-generation autonomous and industrial robotics.
Research Focus
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