Weiwei Guang
Papers
2
Total Citations
143
H-Index
2
About
Weiwei Guang is a leading researcher in advanced control theory, specializing in stochastic nonlinear multiagent systems, fault-tolerant control, and optimal containment strategies. Her work addresses critical challenges in ensuring stability and performance under complex, real-world constraints such as non-affine faults, unknown hysteresis, and communication delays. Guang’s major contributions include pioneering optimized adaptive finite-time consensus control, as demonstrated in her highly cited 2023 paper (120 citations), which integrates neural-network-based reinforcement learning with backstepping to achieve rapid, energy-efficient coordination despite actuator faults. She further advanced the field with her 2024 study on fixed-time optimal bipartite containment control (23 citations), solving the Hamilton-Jacobi-Bellman equation to manage Bouc-Wen hysteresis in stochastic environments. Her research is notable for bridging theoretical rigor with practical applicability, offering robust solutions for autonomous systems, robotics, and networked control. With a growing citation impact and a focus on fixed-time convergence and optimization, Guang is shaping the next generation of resilient, intelligent multiagent systems, making her work essential reading for students and engineers tackling nonlinear, uncertain dynamics.
Research Focus
Key Achievements
Top Papers
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