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About

Guangzheng Peng is a pioneering researcher in the field of pneumatic robotics, with a career dedicated to solving the fundamental challenges of modeling and controlling highly nonlinear, time-varying pneumatic systems. His work is centered on developing intelligent control strategies—including dynamic neural networks, fuzzy PID algorithms, and internal model controllers—to achieve precise motion and position control for pneumatic manipulators and servo systems. Peng’s most cited work, "Modeling and control for pneumatic manipulator based on dynamic neural network" (2004, 19 citations), provides a foundational approach to handling the complex dynamics of single-rod pneumatic actuators. He has also made notable contributions to soft robotics, designing a dexterous hand actuated by pneumatic muscle actuators that offers both compliance and dexterity, controlled via a Fuzz-PID scheme. Beyond actuation, Peng has advanced autonomous robot perception by applying evidential theory to fuse data from multiple ultrasonic sensors for more reliable environment sensing. His research, spanning from foundational control theory to practical robotic hands, has accumulated over 35 citations, establishing him as a key figure in intelligent pneumatic actuation and control.