Shuhui Li

Papers

Total Citations

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About

Shuhui Li is a researcher whose work bridges the critical intersection of industrial robotics and advanced electric motor control. His primary research areas include optimal trajectory planning for robotic systems, intelligent control of interior permanent magnet (IPM) motors, and autonomous vehicle dynamics. Li’s most impactful contribution is his 2018 paper on optimal trajectory planning for industrial robots, which has garnered 31 citations and addresses the fundamental challenge of path tracking with arbitrary position and orientation constraints—a key problem for manufacturing automation. Building on this foundation, Li has pioneered the application of neural networks with cloud-based training for IPM motor control, a 2023 work with 11 citations that tackles the complex MTPA, flux-weakening, and MTPV techniques essential for electric vehicles, robots, and drones. His most recent 2024 paper on adhesion coefficient identification for wheeled mobile robots on unstructured pavement, using extended Kalman filters, demonstrates his commitment to solving real-world autonomy challenges. Li’s work is notable for its practical impact on electric vehicle efficiency and robotic mobility, making him a valuable contributor to the fields of mechatronics and intelligent control systems.