Zhong Ouyang

Papers

Total Citations

148

H-Index

About

Dr. Zhong Ouyang is a leading figure in intelligent control systems, whose work is reshaping the field of robotic manipulation through advanced neural network architectures. His primary research focuses on adaptive control theory, particularly the development of Radial Basis Function Neural Networks (RBFNNs) for nonlinear systems. Dr. Ouyang’s most influential contribution is his pioneering "adaptive bias RBF neural network control" for robotic manipulators, a work that has garnered 116 citations for its novel approach to overcoming the inherent limitations of conventional adaptive controllers. He further advanced the discipline by proposing a composite adaptive RBFNN control with an optimized hidden node distribution, directly addressing three critical demerits of standard designs—including the difficulty in determining the approximation domain—as detailed in his highly regarded 2021 paper (32 citations). By systematically solving the challenge of hidden node lattice distribution, Dr. Ouyang has provided a more robust and efficient framework for real-time robotic control. His research not only pushes the theoretical boundaries of adaptive neural networks but also offers practical solutions for precision engineering, making him a vital contributor to the next generation of intelligent autonomous systems.