Ying Dai

Iwate Prefectural University, Shenyang Ligong University, NARI Group (China)

Papers

Total Citations

H-Index

About

Ying Dai is a robotics researcher whose work centers on control theory, nonlinear dynamics, and intelligent automation for robotic manipulators and inspection systems. Her most significant contribution lies in developing robust and adaptive control strategies that address the inherent nonlinearities and uncertainties in robotic systems. In her highly cited 2002 paper, Dai proposed a globally convergent robust controller for robot manipulators, leveraging Lyapunov stability theory to ensure tracking performance despite model imperfections—a foundational approach that has influenced subsequent work in the field. She further advanced this line of research in 2016 by integrating RBF neural network compensation with adaptive control, enhancing trajectory accuracy for complex manipulator tasks. More recently, Dai has applied her expertise to practical automation challenges, developing a hierarchical map-based method for rapidly configuring inspection objects for substation inspection robots, reducing setup time and labor. With a cumulative citation count reflecting steady engagement from the control and robotics communities, Dai’s work bridges theoretical rigor and real-world deployment, making her a notable contributor to the evolution of intelligent robotic systems.