Shuhai Quan

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About

Shuhai Quan is a leading researcher in robotics and intelligent control systems, with a focus on optimizing autonomous machine performance. His work bridges thermal spraying automation and advanced energy management for mobile robots. In his highly cited 2015 paper (34 citations), Quan developed a computer-aided strategy for robot trajectory auto-generation in thermal spraying, significantly improving coating precision and process efficiency. He further advanced the field with a 2014 study on nonlinear recurrent neural network predictive control for fuel cell powered robots, where he modeled complex power systems using time-variant ARMAX structures. This work enabled optimal energy distribution between fuel cells and ultracapacitors, enhancing robot endurance and reliability. Quan’s contributions have been instrumental in integrating neural network control with hybrid power systems, earning recognition for practical applications in industrial robotics and sustainable energy. His research continues to influence the development of intelligent, energy-efficient autonomous systems, making him a notable figure in control engineering and robotics.