Xingqiang Zhao
Papers
2
Total Citations
47
H-Index
2
About
Xingqiang Zhao is a leading researcher in advanced control systems for industrial robotics, specializing in adaptive and neural network-based approaches to address critical challenges in robotic manipulator performance. His primary research areas include sliding mode control, neural network adaptation, and robust controller design for systems operating under uncertain and varying conditions. Zhao’s major contributions lie in developing innovative switched controller architectures that integrate neural networks with sliding mode techniques to achieve precise trajectory tracking for robotic manipulators handling switching loads. His 2022 work on the SNNSMC scheme, cited 32 times, introduced a novel method to mitigate error accumulation and system instability, a persistent issue in industrial automation. In 2023, he advanced this line of inquiry by addressing the combined challenges of varying loads and unknown dead-zone nonlinearities, achieving 15 citations for his adaptive controller design. Zhao’s research directly impacts real-world manufacturing and automation, offering robust, intelligent solutions for robots operating in unpredictable environments. His work is recognized for bridging theoretical control theory with practical implementation, making him a notable figure in the field of robotic control systems.
Research Focus
Key Achievements
Top Papers
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