Quanxin Zhu
Papers
6
Total Citations
63
H-Index
4
About
Quanxin Zhu is a leading figure in stochastic control theory and nonlinear dynamics, with a particular focus on the stability and performance of complex systems under uncertainty. His work bridges advanced mathematical frameworks—such as semi-Markov jump processes, Takagi–Sugeno fuzzy models, and zeroing neural networks—with practical control strategies like event-triggered and intermittent feedback mechanisms. Zhu’s most cited paper, a 2021 study on finite-time zeroing neural network models for time-variant quadratic programming, has garnered 43 citations and demonstrates his ability to solve constrained optimization problems with constant or fuzzy parameters. His recent contributions, including dual switching dynamic event-triggered controls and almost sure exponential stability analyses for stochastic fuzzy systems, have each attracted 5–6 citations, reflecting growing interest in his innovative approaches to reducing communication and computation loads while ensuring robust stability. Zhu’s work on dynamic predictor-based event-triggered control for stochastic systems with time-varying output delay further underscores his impact, offering novel solutions to real-world challenges in networked control. With a consistent record of high-quality publications, Zhu is shaping the future of intelligent, resilient control systems.
Research Focus
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