Papers
7
Total Citations
27
H-Index
3
About
Qunpo Liu is a robotics and control systems researcher whose work spans intelligent control, trajectory optimization, and robotic manipulator design. With a career bridging environmental robotics in the early 2010s — evidenced by pioneering work on double spiral propulsion robots for wetland navigation — to sophisticated modern control methodologies, Liu has developed a distinctive research portfolio centered on uncertain and constrained robotic systems. Liu's most significant contributions lie in advanced control theory for robotic manipulators, particularly adaptive iterative learning control (AILC) using radial basis function neural networks, prescribed performance control frameworks, and fault-tolerant strategies for actuator failures. His work directly addresses real-world challenges such as initial state errors, full-state constraints, and external disturbances, offering robust solutions for long-stroke hybrid robots and multi-degree-of-freedom manipulators. A notably creative application is his pulse diagnosis robot — a four-DOF manipulator designed to replicate traditional Chinese medical pulse examination — reflecting his interest in translating control precision into healthcare contexts. More recently, Liu has advanced multi-objective trajectory optimization using evolutionary particle swarm methods. Though an emerging scholar with citations accumulating since 2019, his recent papers are already attracting attention, signaling growing influence within the robotics control community.
Research Focus
Key Achievements
Top Papers
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