Guofei Xiang
Papers
9
Total Citations
140
H-Index
7
About
Guofei Xiang is at the forefront of intelligent robotic control, pioneering advanced algorithms that enable robots to learn, adapt, and operate with unprecedented autonomy in complex, real-world environments. His research masterfully integrates deep reinforcement learning, adaptive dynamic programming, and disturbance-rejection control to solve fundamental challenges in robotic skill acquisition and trajectory tracking. Xiang’s seminal 2019 work on task-oriented deep reinforcement learning (53 citations) established a framework for efficient robotic skill learning, directly addressing the critical bottleneck of extensive environmental interactions. He has further advanced the field through robust control strategies for specialized platforms, including magnetic wheeled mobile robots and cable-driven continuum robots, with his 2022 paper on disturbance-rejection control for soft robots (12 citations) providing novel parameterization methods for these highly nonlinear systems. His innovative application of model predictive control for transformer inspection robots (2023) and T-S fuzzy quaternion-value neural networks for mecanum mobile robots demonstrates a remarkable ability to tailor sophisticated control theory to practical industrial needs. With over 140 total citations, Xiang’s work is not only highly cited but also directly translatable to safety-critical applications in manufacturing, electrical infrastructure, and confined-space inspection, marking him as a leading architect of next-generation autonomous robotic systems.
Research Focus
Key Achievements
Top Papers
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- 3Intelligence evolution for service robot: An ADRC perspective15 citations · 2018
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