Chenyang Shao

Central South University of Forestry and Technology

Papers

Total Citations

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About

Chenyang Shao is a researcher focused on advancing robotic actuation and control systems, with a particular emphasis on series elastic drive joints. His work integrates adaptive control theory with neural network optimization to enhance the performance and safety of robotic systems. Shao’s most-cited paper, "Adaptive Control of Robot Series Elastic Drive Joint Based on Optimized Radial Basis Function Neural Network" (2021), has garnered 11 citations, demonstrating its relevance in the field of compliant robotics. This contribution addresses critical challenges in achieving precise, stable, and adaptive motion control in robots that interact with dynamic environments, such as collaborative and rehabilitation robots. By optimizing radial basis function neural networks, Shao’s approach improves torque tracking and disturbance rejection, offering a robust solution for elastic actuators. His research bridges theoretical control methods with practical robotic applications, making strides toward more intelligent and adaptable machines. Shao’s work is particularly valuable for engineers and researchers developing next-generation robots that require both flexibility and precision, positioning him as a notable contributor to the evolving landscape of adaptive robotic control.