Papers
35
Total Citations
871
H-Index
16
About
Peijiang Yuan is a prominent robotics and control systems researcher whose work spans intelligent robot control, precision manufacturing automation, and neural network-based optimization. His research has made substantial contributions across two primary domains: advanced control strategies for mobile and legged robots, and high-precision robotic systems for aerospace manufacturing applications. Yuan's early work on model predictive control using neurodynamics optimization and sensory-motor fusion for robotic hand-eye systems — each garnering 143 citations — established him as a significant voice in intelligent robot control. His development of a robust MPC scheme for wheeled mobile robots and a hierarchical vision-sEMG control architecture for grasping demonstrated his ability to bridge theoretical control design with practical implementation. He further extended this to quadruped robots, addressing dynamic balance optimization and energy-efficient locomotion. Equally notable is Yuan's sustained focus on enhancing industrial and aviation drilling robot accuracy. Through a series of innovative compensation methods — leveraging radial basis function neural networks, Co-Kriging, extreme learning machines, and error similarity principles — he has systematically addressed positioning inaccuracies critical to aircraft manufacturing quality and safety. His compact aero-robotic end-effector design and surface-normal measurement innovations further demonstrate a rare combination of algorithmic sophistication and engineering pragmatism, making his body of work highly relevant to both academic researchers and industry practitioners.
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