Yongkun Sun

University of Science and Technology Beijing

Papers

Total Citations

225

H-Index

About

Yongkun Sun is a leading researcher in the control and automation of advanced robotic systems, with a primary focus on flexible-joint manipulators and series elastic actuator (SEA)-driven robots. His major contributions lie in developing intelligent, neural-learning-based control strategies to overcome the inherent uncertainties and nonlinearities in these complex dynamic systems. Sun’s most influential work, “Neural-Learning-Based Control for a Constrained Robotic Manipulator With Flexible Joints” (2018), has garnered 175 citations, addressing a critical gap in mature control technology for flexible-joint manipulators. This paper proposes a novel framework that effectively handles system uncertainties, significantly advancing the field. Further demonstrating his impact, Sun’s “Adaptive NN impedance control for an SEA-driven robot” (2020, 50 citations) extends his expertise to impedance control, enhancing safety and adaptability in human-robot interaction. Through these contributions, Sun has established himself as a key figure in bridging neural network theory with practical robotic control, offering robust solutions that push the boundaries of what constrained, flexible robotic systems can achieve.