Zhengyu Liu

Tsinghua University, Beihang University, University of Essex

Papers

Total Citations

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About

Zhengyu Liu is a researcher whose work bridges the frontiers of control theory and robotics, with a focus on developing intelligent, efficient systems for autonomous motion. His most significant contribution is the introduction of **Recurrent Model Predictive Control (RMPC)** , a groundbreaking offline algorithm that learns an explicit recurrent controller for large-scale nonlinear systems. This approach effectively acts as an explicit solver for traditional Model Predictive Control (MPC), enabling adaptive and computationally efficient real-time control without the need for online optimization. The work, published in 2022, has already garnered **14 citations**, signaling its growing influence in the field of nonlinear control and machine learning. Earlier in his career, Liu tackled practical challenges in robotics, designing a **pure rolling steering system** for wheeled mobile robots that eliminates side slip—a common issue with conventional steering trapeziums—by leveraging synchronous belt technology for precise, long-distance transmission. This innovation, presented in 2019, demonstrates his ability to solve real-world mechanical problems. Liu’s research trajectory, from mechanical design to advanced algorithmic control, highlights a commitment to both theoretical depth and practical impact, making his work essential reading for students and researchers in robotics, control systems, and autonomous navigation.