Xuemei Ren

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About

Xuemei Ren is a control systems researcher whose work spans intelligent control, nonlinear systems identification, and precision servo control. Her research has made meaningful contributions to the development of adaptive neural network-based methodologies for modeling and controlling complex nonlinear systems, as well as advancing repetitive control strategies for servo systems operating under time delay constraints. One of her most recognized contributions is her 2013 work on repetitive control of servo systems with time delays, which has garnered 44 citations and reflects the practical significance of her approach in precision motion control applications. Her earlier work from 2003 on identification and control of nonlinear systems using dynamic neural networks laid foundational groundwork in applying adaptive neural architectures to continuous-time systems, where a dynamic neural network is employed both for system identification and as the basis for subsequent controller design — a two-stage framework that influenced later research in intelligent control. Ren's research bridges theoretical rigor with engineering applicability, making her work particularly relevant to robotics, manufacturing automation, and embedded control systems. Students and engineers working on adaptive control and intelligent servo design will find her contributions a valuable reference point in the field.