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About

Qingze Zou is a researcher whose work spans control systems theory, advanced manufacturing, and intelligent automation. His foundational contributions in control engineering are exemplified by his highly cited 2012 work on inversion-based optimal output tracking for nonminimum-phase linear systems, which garnered 29 citations and addressed critical challenges in precise trajectory control—a cornerstone problem in fields ranging from robotics to precision manufacturing. More recently, Zou has pushed the boundaries of automated industrial systems, pioneering efforts to integrate computer vision and non-prehensile manipulation for fully automated metal recycling, tackling the notoriously difficult problem of separating scrap metals with physically attached impurities. His forward-looking research has also embraced next-generation connectivity, with multiple studies exploring how 5G wireless communication and cloud/edge computing can enable real-time process monitoring in milling operations and robotic part repairing in advanced manufacturing contexts. These emerging contributions, already attracting early citation attention, signal a researcher actively shaping the future of smart manufacturing. Across his career, Zou consistently bridges rigorous mathematical control theory with practical, high-impact industrial applications, making his work highly relevant to engineers and researchers working at the intersection of automation, robotics, and intelligent manufacturing systems.