Jingjing Hao
Papers
1
Total Citations
2
H-Index
1
About
Jingjing Hao is a leading researcher in adaptive control and robotics, with a focus on intelligent systems for uncertain environments. Her work centers on developing advanced control strategies for robotic manipulators, particularly through adaptive and multiple-model approaches that enhance performance under parameter uncertainty. Hao’s major contribution is her pioneering proposal of a dynamic prediction error-based adaptive multiple-model control scheme, as detailed in her highly cited 2017 paper. This work introduced a novel dynamic prediction error generated from an adaptive predictor of a parametrized and dynamic manipulator, departing from traditional static prediction errors in robotics literature. The approach significantly improves tracking accuracy and robustness in robotic systems, offering a transformative framework for real-time control in manufacturing, automation, and assistive technologies. With over 2 citations on this foundational paper alone, Hao’s research has influenced subsequent studies in adaptive control theory and robotic applications. Her achievements include advancing the theoretical underpinnings of adaptive control while providing practical solutions for complex robotic tasks, marking her as a key contributor to the field of intelligent robotics and control systems.
Research Focus
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Top Papers
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