Zhengui Xue

National University of Singapore, South China University of Technology

Papers

Total Citations

H-Index

About

Zhengui Xue is a leading researcher in the fields of adaptive control, neural network (NN) systems, and robotic manipulation. His work focuses on bridging the gap between control theory and machine learning, particularly through the lens of deterministic learning—a framework that enables dynamic systems to achieve locally accurate approximation of unknown dynamics during periodic tracking tasks. Xue’s major contributions include pioneering the integration of robust adaptive NN control with deterministic learning theory for robot manipulators, demonstrating that even under unknown system dynamics and disturbances, a controller can both stabilize the system and learn its underlying dynamics. His most-cited papers, such as "Deterministic learning from robust adaptive NN control of robot manipulators" (7 citations) and "Deterministic learning and robot manipulator control" (6 citations), have laid foundational groundwork for intelligent robotic systems that adapt and learn in real-time. These works are notable for their rigorous theoretical proofs and practical implications, offering a pathway toward more autonomous and resilient robots. Xue’s research continues to influence students and engineers working at the intersection of nonlinear control, neural networks, and robotics.