Mingxin Liu

Wuhan University of Science and Technology, Shandong University of Science and Technology

Papers

Total Citations

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About

Mingxin Liu is a rising researcher in robotics and intelligent control systems, with a focus on advancing the modeling and control of uncertain mechanical systems. Their work bridges deep learning and robust control theory to address critical challenges in robotic manipulation. Liu’s most cited paper, “Dynamic Model Learning for Robotic Manipulators using BiLSTM Networks” (2022, 3 citations), introduces a novel approach that leverages bidirectional long short-term memory networks to capture future state dependencies—overcoming a key limitation in prior inverse dynamic modeling. This work enhances the accuracy and adaptability of manipulator control in complex environments. In their more recent study, “Robust Control Design of Uncertain Mechanical Systems Based on the Universal Control Performance Metric” (2024, 2 citations), Liu proposes a unified framework for designing controllers that maintain performance under uncertainty, offering a practical tool for real-world robotic applications. Though early in their career, Liu’s contributions demonstrate a clear trajectory toward integrating data-driven learning with rigorous control guarantees, making their work relevant for students and researchers exploring next-generation autonomous systems.