Zunhao Zhang

Tianjin University of Science and Technology

Papers

Total Citations

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About

Zunhao Zhang’s research lies at the intersection of rehabilitation robotics, adaptive control theory, and neural network intelligence. His most influential work, “Design of RBFNN-Based Adaptive Sliding Mode Control Strategy for Active Rehabilitation Robot” (2020, 38 citations), introduces a novel control framework that leverages radial basis function neural networks (RBFNN) to enable adaptive sliding mode control. By detecting movement signals from a patient’s non-affected limb, Zhang’s strategy allows rehabilitation robots to mirror natural gait coordination, offering a more intuitive and responsive therapy for lower-limb recovery. This contribution is pivotal for advancing human–robot interaction in clinical settings, where precision and adaptability are critical. Beyond this flagship paper, Zhang’s broader portfolio explores intelligent control systems and bio-inspired robotics, consistently bridging theoretical control design with practical rehabilitation needs. His work has garnered attention from both engineering and medical communities, underscoring its translational impact. For students and researchers, Zhang exemplifies how neural adaptive methods can transform assistive technologies, making robotic rehabilitation safer, more effective, and patient-centered.