Ma Jing Ma Jing

Papers

Total Citations

H-Index

About

Ma Jing is a researcher whose work sits at the intersection of robotics, adaptive control, and neural network theory. Her most cited paper, "Adaptive Control for Robotic Manipulators based on RBF Neural Network" (2013), addresses a fundamental challenge in robotics: achieving precise trajectory tracking in the presence of system uncertainties. In this work, she proposes a novel adaptive controller that combines a PD feedback loop with a dynamic compensator—integrating a radial basis function (RBF) neural network and variable structure control. This hybrid approach offers a robust solution for real-time control of robotic manipulators, balancing stability and adaptability. While her citation count (5) reflects a focused, emerging impact, the paper’s contribution lies in its practical, computationally efficient framework for uncertain nonlinear systems. Ma’s research is particularly valuable for students and engineers working on intelligent control systems, as it demonstrates how neural networks can be effectively embedded into classical control architectures. Her work continues to inform developments in adaptive robotics and smart manufacturing.