Zihang Guo

Papers

Total Citations

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About

Zihang Guo is a rising researcher in the fields of multiagent systems, adaptive control, and human-robot interaction. His work focuses on developing intelligent, safe, and efficient control strategies for autonomous systems, particularly in complex and dynamic environments. Guo’s most-cited paper, “Neural observer-based fixed-time formation control of multiagent systems” (2024, 8 citations), introduces a novel approach to achieving rapid and stable coordination among multiple agents, a critical challenge in applications like drone swarms and autonomous vehicles. Building on this, his recent work “Adaptive hierarchical control of quadcopters via safe reinforcement learning from human demonstration” (2025, 6 citations) pioneers a framework that combines human expertise with machine learning to ensure safety while optimizing performance in real-time quadcopter control. These contributions demonstrate his ability to bridge theoretical control theory with practical, safety-critical implementations. Though early in his career, Guo’s research is already gaining traction for its innovative integration of neural networks, fixed-time convergence, and human-in-the-loop learning, positioning him as a promising voice in the future of autonomous systems and multiagent coordination.