Ruihang Ji

National University of Singapore, Harbin Institute of Technology

Papers

Total Citations

123

H-Index

About

Ruihang Ji is a robotics researcher whose work focuses on advancing the control and coordination of complex robotic systems, from single manipulators to multi-agent teams. His primary research areas include adaptive neural network control, robotic manipulator dynamics, and multi-agent reinforcement learning (MARL). Ji’s most significant contribution is the development of an adaptive bias radial basis function (RBF) neural network controller for robotic manipulators, a paper that has garnered 116 citations and demonstrates a robust method for handling system uncertainties and disturbances. He further extended this approach to airborne robotic manipulators (ARM), using RBF neural networks to approximate unknown dynamics and improve stability in challenging aerial environments. In more recent work, Ji has tackled practical industrial challenges, such as designing a specialized tracking controller with an adjustable trajectory envelope for cable-driven robots to address torque limitations and overload issues. He has also explored MARL strategies for multi-agent negotiation in manipulation tasks, contributing to the field of cooperative robotics. Ji’s research is characterized by a strong emphasis on real-world applicability, bridging theoretical control methods with the practical demands of robotic systems in industrial and airborne settings.