Pengfei Ou

Papers

Total Citations

H-Index

About

Pengfei Ou’s research bridges the frontiers of robotics and materials science, with a focus on vibration control in multi-modal robotic systems and the computational discovery of next-generation battery materials. In his most-cited work, Ou developed a hybrid input shaping control scheme that effectively reduces residual vibration in robots by combining positive and negative impulses tailored to multiple resonant modes. This approach significantly improves response time and stability in complex robotic systems, offering practical solutions for precision automation. His contributions in robotics are complemented by a forward-looking review on AI agents for solid electrolytes, which explores how machine learning and autonomous systems can accelerate the discovery and optimization of safer, high-performance battery materials. This work highlights Ou’s versatility and his ability to identify transformative opportunities at the intersection of artificial intelligence and energy storage. With over a dozen citations across his key publications, Ou’s research demonstrates both technical depth and interdisciplinary reach. His work on hybrid input shaping remains a valuable reference for engineers tackling multi-modal vibration challenges, while his insights into AI-driven materials discovery position him at the forefront of sustainable energy innovation.