Xiong Yang
Papers
2
Total Citations
143
H-Index
2
About
Xiong Yang is a researcher specializing in intelligent control systems, reinforcement learning-based optimization, and nonlinear control theory. His work sits at the intersection of adaptive control and machine learning, focusing on developing advanced control strategies for complex, uncertain dynamical systems. Yang's most prominent contribution is his 2017 work on event-triggered optimal neuro-controllers, which garnered 125 citations and introduced a novel approach to controlling unknown nonlinear continuous-time systems. By combining reinforcement learning with an event-triggering mechanism, his framework departs from traditional time-triggered control paradigms, updating controllers only when system states deviate beyond a defined threshold — a computationally efficient and practically significant innovation. This work has had notable influence in the fields of adaptive dynamic programming and intelligent control. Building on this foundation, his 2022 research extended these ideas to robust tracking control, addressing the challenging problem of uncertain nonlinear systems subject to asymmetric input constraints. By constructing augmented system dynamics and incorporating discounted value functions, Yang demonstrated a rigorous approach to handling real-world control constraints. Collectively, Yang's research advances the theoretical and practical understanding of data-driven, learning-based control design, making meaningful contributions to the growing field of autonomous and intelligent systems engineering.
Research Focus
Key Achievements
Top Papers
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