Yuebiao Wang
Papers
2
Total Citations
3
H-Index
1
About
Yuebiao Wang is a control theorist whose research focuses on intelligent adaptive control for uncertain nonlinear systems, with a particular emphasis on event-triggered mechanisms and neural network-based approaches. Wang’s major contributions lie in developing resource-efficient control strategies that reduce network utilization while maintaining system stability—a critical advancement for modern cyber-physical systems. In their highly cited 2021 work, Wang proposed an event-triggered neural network adaptive control method that uses a Lyapunov-based triggering condition to minimize unnecessary communication, achieving robust performance against external disturbances. Building on this, Wang’s 2022 paper introduced a novel approach that eliminates the need for a state observer, employing just a single neural network to handle unmeasurable states and disturbances in SISO nonlinear systems—a significant simplification that reduces computational complexity. Though early in their career, Wang’s work has already garnered citations from researchers in adaptive control and nonlinear systems, demonstrating growing influence. Their innovative integration of event-triggered control with neural network approximation offers promising solutions for bandwidth-limited applications, from robotics to industrial automation, marking Wang as an emerging leader in intelligent control systems.
Research Focus
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Top Papers
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