Peng Hang

Nanyang Technological University

Papers

1

Total Citations

19

H-Index

1

About

Peng Hang is an emerging researcher specializing in autonomous vehicle systems, intelligent chassis control, and fault-tolerant control technologies for mobile robotic platforms. His work sits at the critical intersection of motion control theory and practical autonomous system safety, addressing one of the most pressing challenges in modern robotics: ensuring reliable and efficient operation under real-world uncertainties and component failures. His most recognized contribution, "Nonlinear Predictive Motion Control for Autonomous Mobile Robots Considering Active Fault-Tolerant Control and Regenerative Braking" (2022), has garnered 19 citations since publication — a strong indicator of early impact within the autonomous systems community. This work introduces an integrated chassis control framework that innovatively combines nonlinear predictive control with active fault-tolerant mechanisms and energy-recovering regenerative braking, simultaneously advancing both vehicle safety and efficiency. By uniting feedforward and feedback control algorithms within a unified longitudinal motion control module, Hang's framework offers a sophisticated yet practical solution for next-generation autonomous mobile robots. Though early in his research trajectory, Peng Hang demonstrates a distinctive ability to bridge advanced control theory with engineering application, positioning him as a promising contributor to the rapidly evolving fields of autonomous mobility and intelligent transportation systems.

Research Focus

Key Achievements

1
H-Index
1
Papers
19
Total Citations
19
Avg Citations/Paper
🏆 Most Cited Paper
Nonlinear Predictive Motion Control for Autonomous Mobile Robots Considering Active Fault-Tolerant Control and Regenerative Braking
19 citations · 2022
📈 Most Prolific Year: 2022 (1 Papers)
🤝 Key Collaborators: 2
🏛 Institutions: Nanyang Technological University

Top Papers

  1. 1

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago