Qiangyong Shi

Papers

Total Citations

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About

Qiangyong Shi’s research lies at the intersection of rehabilitation robotics and intelligent control systems, with a primary focus on restoring mobility for patients with neurological injuries such as stroke or spinal cord injury. His work addresses a critical challenge: developing effective, real-time control strategies for lower limb rehabilitation robots, which are inherently nonlinear and time-varying systems. Shi’s major contributions include proposing an RBF neural network compensation method for trajectory tracking control, which significantly enhances the precision and stability of robotic training movements. He has also designed innovative control strategies tailored to different stages of patient recovery, from passive to active-assistive training, and developed a novel body weight support system that improves patient safety and gait training efficacy. With over 17 citations across his most-cited papers, Shi’s research has provided foundational insights into the practical implementation of rehabilitation robots. His work is particularly notable for bridging the gap between theoretical control algorithms and real-world clinical applications, offering a roadmap for engineers and clinicians seeking to improve the quality of life for individuals with lower limb impairments.