Rongjie Liu

Southeast University, Florida State University

Papers

Total Citations

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About

Rongjie Liu is a control systems researcher whose work focuses on advanced nonlinear control strategies for robotic and complex dynamical systems. His most cited paper (2013, 31 citations) introduces an optimal integral sliding mode control scheme using pseudospectral methods for multi-input multi-output nonlinear systems, specifically applied to rigid robotic manipulators with constraints—a contribution that bridges optimal control theory with practical robotic applications. Liu’s research also extends to disturbance observer-based inverse optimal control (2022, 20 citations), offering robust solutions for systems facing unknown disturbances. More recently, he has explored data-driven approaches, including neural ordinary differential equation (NODE)-based multirate sampled data state feedback control (2023), reflecting a shift toward integrating machine learning with traditional control theory. With a career spanning foundational sliding mode techniques to modern data-driven methods, Liu’s work demonstrates sustained impact in nonlinear control, particularly for robotics and automation. His publications, accumulating over 50 citations, highlight his ability to address both theoretical rigor and real-world implementation challenges, making him a notable contributor to the field of advanced control systems.