Xin‐An Zeng
Papers
1
Total Citations
2
H-Index
1
About
Xin‐An Zeng is a control systems researcher whose work focuses on advanced robotics and intelligent fault-tolerant control, particularly for omnidirectional mobile robots. His most notable contribution is the development of adaptive neural network preset-time control strategies that address critical challenges in robot autonomy, such as input saturation and state constraints. In his landmark 2025 paper, Zeng introduced a novel framework that ensures robust performance even under actuator faults, achieving preset-time convergence—a significant advancement over traditional asymptotic or finite-time methods. This work, already garnering 2 citations shortly after publication, demonstrates his ability to tackle real-world complexities in mobile robotics, where safety and precision are paramount. Zeng’s research integrates adaptive control, neural networks, and Lyapunov theory, offering practical solutions for autonomous systems operating in uncertain environments. His achievements highlight a commitment to pushing the boundaries of fault-tolerant control, making his work highly relevant for researchers in robotics, automation, and intelligent systems.
Research Focus
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Top Papers
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