Max Robotics — Global Service Robot Directory + US Certification

About

Xiongxiong He is a leading researcher in advanced control theory and robotic systems, with a primary focus on iterative learning control, sliding mode control, and data-driven methodologies for nonlinear dynamics. His seminal work, "Repetitive Learning Control for Time-varying Robotic Systems: A Hybrid Learning Scheme" (2007, 22 citations), established foundational techniques for improving robotic precision in repetitive tasks. He has significantly advanced mobile robot navigation, notably through his 2024 paper on a "Local Path Planner for Mobile Robot Considering Future Positions of Obstacles" (11 citations), which introduces an improved timed elastic band (TEB) planner for superior dynamic obstacle avoidance—a critical contribution to autonomous navigation. He also pioneered the integration of bio-inspired intelligence with control, as seen in his work on "Pulse neural network–based adaptive iterative learning control for uncertain robots" (2012, 8 citations). His recent research on "Data-Driven Control With Prescribed-Time Convergence for Discrete-Time Nonlinear Systems" (2024, 6 citations) pushes the boundaries of model-free adaptive control, ensuring precise convergence within a user-defined timeframe. Additionally, his development of a "Nonsingular and fast convergent terminal sliding mode control of robotic manipulators" (2011) offers a robust solution for finite-time tracking error elimination. With a career spanning foundational theory to cutting-edge applications, He’s work is essential reading for researchers in robotics, nonlinear control, and autonomous systems.