Shengchuang Guan
Papers
3
Total Citations
75
H-Index
3
About
Shengchuang Guan is a rising researcher at the forefront of soft robotics and intelligent control systems. His work focuses on addressing the fundamental challenges of modeling and controlling highly deformable, nonlinear robotic systems—particularly soft pneumatic actuators (SPAs) and robotic manipulators. Guan’s major contributions include pioneering data-driven control strategies that overcome the severe hysteresis nonlinearity inherent in soft actuators. His 2023 paper on hysteresis inversion-free predictive compensation, which leverages a global Koopman modeling strategy, offers a breakthrough approach to achieving precise, real-time control without complex physical models. He further advanced the field with a bilinear model predictive control framework (BKMPC-ESO) that compensates for unknown nonlinear dynamics, demonstrating robust performance in soft robots. Guan’s most cited work, a comprehensive 2023 review on artificial neural networks for robotic manipulators (63 citations), has become a key reference for researchers integrating machine learning into robotic control. His innovative fusion of data-driven modeling, predictive control, and soft robotics positions him as a leading voice in next-generation, compliant robotic systems.
Research Focus
Key Achievements
Top Papers
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