Papers
8
Total Citations
120
H-Index
6
About
Xingyu Zhou is a leading researcher in advanced control systems for rigid-flexible coupled robotic mechanisms, a field critical for next-generation automation in manufacturing, aerospace, and service robotics. His work focuses on overcoming fundamental challenges in these systems, including actuator faults, unknown control directions, input quantization, time delays, and large beam deflections. Zhou’s major contributions include developing robust adaptive fault-tolerant control using RBF neural networks, neural network state observers for iterative learning control, and fixed-time trajectory tracking controllers that ensure stability and vibration suppression under extreme conditions. His most cited paper (2021, 35 citations) pioneered a robust adaptive fault-tolerant control framework for rigid-flexible systems, while subsequent works on prescribed performance control and input quantization have garnered 24, 17, and 16 citations respectively. Zhou also extends his expertise to mobile robotics, with a 2025 paper on dynamic model predictive control for three-wheeled independent drive and steering robots. His innovative integration of neural networks, fuzzy reinforcement learning, and nonlinear disturbance observers has produced over 120 total citations, establishing him as a key figure in intelligent control theory for flexible robotic systems.
Research Focus
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