Cheng Hua
Papers
4
Total Citations
105
H-Index
3
About
Cheng Hua is a rising leader in multi-robot coordination and bio-inspired neural control, whose work addresses the critical challenge of enabling robot teams to adapt to uncertain, dynamic environments in real time. His primary research areas include zeroing neural dynamics, multi-robot formation control, and noise-tolerant optimization. Hua’s most impactful contribution is the development of a zeroing neural dynamics approach for inter-robot management via neighboring robot sensing and measurement, a paper that has garnered 87 citations and provides a robust framework for decentralized robot coordination. He has further advanced the field with a prescribed-time convergence noise-tolerant zeroing neural network for multi-robot position management, achieving finite-time formation control with high precision. His 2025 work on optimization-based finite-time multi-robot formation introduces a method that outperforms traditional approaches in handling uncertain behaviors. Additionally, his comprehensive review of advances in zeroing neural networks highlights bio-inspired structures and performance enhancements, solidifying his role in bridging neural computation and robotics. With a growing citation footprint, Hua’s research is shaping the future of autonomous multi-robot systems for applications ranging from search-and-rescue to industrial automation.
Research Focus
Key Achievements
Top Papers
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