Zhiguan Huang

Papers

Total Citations

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About

Zhiguan Huang is a leading researcher in neural dynamics and intelligent robotic control, with a focus on developing noise-resistant computational models and multirobot coordination systems. His most cited work introduces a groundbreaking discrete-time Z-type neural dynamics model for solving time-dependent Lyapunov equations, achieving robust performance in noisy environments—a critical advancement over traditional noise-free approaches. This contribution has garnered 20 citations, establishing a foundation for reliable real-time computation in engineering applications. Huang further advanced robotic visual servoing by designing a gradient-based recurrent neural network that enables robot manipulators to execute acceleration commands with precision, a work cited 17 times for its impact on vision-guided automation. In multirobot systems, he developed centralized and distributed coordination models for competitive target tracking, demonstrating theoretical guarantees for all-to-all communication and practical scalability with limited connectivity, earning 15 citations. His research bridges theoretical neural dynamics with practical robotics, offering noise-tolerant algorithms and intelligent controllers that enhance the adaptability and efficiency of autonomous systems. Huang’s work is essential reading for students and researchers exploring neural computation, robot control, and multi-agent coordination.