Longwang Huang
Papers
2
Total Citations
9
H-Index
2
About
Longwang Huang is a leading researcher in adaptive neural control and event-triggered systems, with a focus on discrete-time nonlinear dynamics and robotic manipulators. His work addresses critical challenges in networked control systems, particularly the intersection of unmeasurable states, unknown dynamics, and limited communication resources. Huang’s major contributions include pioneering observer-based adaptive neural output-feedback event-triggered control schemes, where he introduced a variable substitution method and i-step ahead predictors to enable stable control under network constraints. His 2021 paper on this topic has garnered 5 citations, while his 2020 study on event-triggered output feedback control for robot manipulators—which employs neural state observers to estimate unmeasured joint velocities—has received 4 citations. These works are notable for advancing the practical deployment of intelligent control in bandwidth-limited environments, offering rigorous solutions for uncertain strict-feedback systems. Huang’s research is highly relevant for students and engineers working on autonomous robotics, networked control, and adaptive systems, providing foundational techniques for reducing communication overhead while maintaining stability and performance.
Research Focus
Key Achievements
Top Papers
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