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About

Shen Zeng is a leading researcher at the intersection of edge computing, industrial automation, and nonlinear control systems. His work fundamentally reimagines how industrial control architectures can leverage modern computational paradigms to overcome traditional limitations. Zeng's most impactful contribution, "Exploring Edge Computing for Multitier Industrial Control" (42 citations), pioneers a distributed two-tier computing framework that replaces conventional microcontroller-based systems with edge-enabled architectures, dramatically improving computational flexibility in industrial settings. He further advances this vision in "Data-Driven Edge Offloading for Wireless Control Systems" (9 citations), addressing the critical challenge of reliable wireless connectivity in multitier cyber-physical systems. In the domain of autonomous systems, Zeng's "Sampling-Based Nonlinear MPC of Neural Network Dynamics" (20 citations) introduces a novel approach to controlling machine learning models, with direct applications to autonomous vehicle motion planning. His work on "Iterative optimal control synthesis for nonlinear switching systems" (4 citations) provides rigorous computational solutions for systems with irregular switching behaviors. Collectively, Zeng's research bridges theoretical control theory with practical edge computing implementations, establishing new paradigms for next-generation industrial automation and autonomous systems.