Lingxi Peng
Papers
2
Total Citations
8
H-Index
1
About
Lingxi Peng is a leading researcher in intelligent robotics and control systems, with a primary focus on fault-tolerant control and machine vision-guided manipulation. His most cited work, "Finite-Time Neural Network Fault-Tolerant Control for Robotic Manipulators under Multiple Constraints" (2022, 7 citations), introduces a groundbreaking backstepping-based controller that integrates barrier Lyapunov functions to ensure robotic manipulators operate safely under time-varying output constraints and actuator saturation. This contribution is pivotal for enhancing reliability in real-world automation, particularly where precision and safety are critical. Peng further advances practical robotics with his work on "A Grasping System with Structured Light 3D Machine Vision Guided Strategy Optimization" (2023), demonstrating expertise in merging 3D vision with control algorithms to optimize grasping strategies. His research bridges theoretical control theory and applied robotics, offering robust solutions for constrained environments. With a growing citation impact, Peng’s work is essential reading for students and engineers seeking to understand fault-tolerant control and vision-based automation. His achievements underscore a commitment to developing intelligent, resilient robotic systems for industrial and service applications.
Research Focus
Key Achievements
Top Papers
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- 2