Fenglan Wang
Papers
2
Total Citations
22
H-Index
2
About
Fenglan Wang is a rising authority in the control and security of complex nonlinear systems, with a primary focus on adaptive neural network control, event-triggered communication, and cyber-physical resilience. Her research addresses a critical challenge in modern automation: how to maintain stable, efficient control when systems are vulnerable to cyberattacks and limited by communication bandwidth. In her highly cited 2024 work, Wang pioneered a switching event-triggered adaptive neural network output-feedback control scheme for switched nonlinear systems subjected to hybrid deception and denial-of-service (DoS) attacks—introducing the novel concept of “effective” DoS attacks to model realistic sensor-to-channel disruptions. Building on this, her 2025 study advanced the field by developing a multiple event-triggering communication framework that employs self-growing and pruning neural networks to handle unknown nonlinearities while reducing unnecessary data transmission. Both papers have already garnered 11 citations each, reflecting their immediate impact on the research community. Wang’s work is particularly notable for bridging theoretical rigor with practical cybersecurity concerns, offering scalable solutions for networked control systems in autonomous vehicles, smart grids, and industrial IoT. Her contributions are shaping the next generation of resilient, resource-aware control architectures.
Research Focus
Key Achievements
Top Papers
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- 2