Papers
8
Total Citations
77
H-Index
5
About
Zhuping Liu is a leading researcher in the field of soft robotics and intelligent control systems, with a primary focus on pneumatic artificial muscle (PAM) actuators and soft robotic systems. Their major contributions lie in developing advanced control strategies—including prescribed-time adaptive fuzzy control, reinforcement learning, and concurrent learning-based adaptive control—to address the inherent nonlinearities, hysteresis, and input constraints of PAM-driven robots. Notably, Liu pioneered a reinforcement-learning-based robust force control method for compliant grinding, achieving high-precision force regulation via inverse hysteresis compensation, and designed a novel three-dimensional deformation pneumatic soft actuator with mutually vertical PneuNets, enabling unprecedented spatial deformation capabilities. Their work on a crocodile-like pneumatic soft crawling robot further demonstrates practical applications in disaster relief and exploration. With over 77 total citations across their most-cited papers, Liu’s research has significantly advanced the theoretical foundations and practical implementations of soft robotics, particularly in achieving guaranteed transient performance and force-sensorless control. Their innovative actuator designs and control frameworks are widely recognized for bridging the gap between theoretical control guarantees and real-world robotic applications.
Research Focus
Key Achievements
Top Papers
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