Zengpeng Lu
Papers
9
Total Citations
73
H-Index
5
About
Zengpeng Lu is a leading researcher in the field of modular and reconfigurable robotics, with a primary focus on decentralized control, fault tolerance, and dynamic parameter identification. His work addresses critical challenges in modular robot manipulators (MRMs), particularly the treatment of interconnection terms, friction, and actuator faults under uncertain conditions. Lu is best known for pioneering adaptive terminal sliding mode control strategies, including integral terminal sliding mode and neural adaptive approaches, which enable robust trajectory tracking and fault-tolerant control even under actuator saturation. His 2019 paper on decentralized trajectory tracking for torque-sensor-equipped MRMs has garnered 20 citations, while his 2022 work on fault-tolerant control via integral terminal sliding mode and disturbance observers has received 14 citations. More recently, his 2024 study on hybrid optimization using genetic algorithms and least squares for dynamic parameter identification has already attracted 10 citations. Lu’s research is distinguished by its experimental validation, bridging theory and practice in robot control. His contributions are essential for advancing the reliability and autonomy of modular robotic systems in industrial and service applications.
Research Focus
Key Achievements
Top Papers
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- 5Stribeck Friction Model Identification Based on Genetic Algorithm6 citations · 2022
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