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About

Zhengxiang Ma is a leading researcher in rehabilitation robotics, specializing in the development of intelligent control systems for lower limb exoskeletons. His work focuses on bridging the gap between human biomechanics and robotic assistance, with key contributions in humanoid control, adaptive trajectory tracking, and gait prediction. Ma’s most influential paper, "Humanoid control of lower limb exoskeleton robot based on human gait data with sliding mode neural network" (27 citations), introduces a novel controller that mimics natural human motion to improve rehabilitation outcomes. He further advanced the field with "Gait Prediction for Rehabilitation Robots Based on Deep Learning" (7 citations), enabling robots to anticipate user movements for smoother human-robot cooperation. His earlier work on adaptive control under model uncertainty and minimum inertial parameters (2019–2020) addressed critical challenges in patient comfort and system robustness. Collectively, Ma’s research has laid a foundation for more intuitive, responsive, and patient-friendly exoskeleton technologies, directly impacting the rehabilitation of individuals with lower limb dysfunction. His innovative integration of neural networks and real human gait data continues to inspire new directions in assistive robotics.