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About

Puwei Lu is a robotics researcher whose work centers on intelligent control systems and bio-inspired mechanical design for advanced robotic platforms. His primary research areas include sliding mode control, adaptive robust control, and deep reinforcement learning for robotic manipulators and legged robots. Lu’s major contributions lie in integrating deep deterministic policy gradient (DDPG) algorithms with traditional control strategies to overcome persistent challenges like chattering and low tracking accuracy in industrial robots. His 2021 paper on nonlinear nonsingular fast terminal sliding mode control (24 citations) demonstrates a novel fusion of reinforcement learning with sliding mode theory, significantly improving response speed and precision. In parallel, Lu has advanced quadruped robot design by developing three-dimensional flexible legs (20 citations) that enhance buffering and terrain adaptability, and he has proposed a modular wall-climbing robot inspired by leech peristalsis, featuring an internal soft bone structure for variable load capacity and gap traversal. His adaptive proportional integral robust control method (12 citations) further showcases his ability to handle model uncertainty and external disturbances in real-time. With over 60 total citations in just a few years, Lu is establishing himself as an innovative voice at the intersection of machine learning and mechanical robotics.