Mingao Lv
Papers
2
Total Citations
12
H-Index
2
About
Mingao Lv is a rising researcher in nonlinear control systems and robotics, with a focus on disturbance estimation and autonomous vehicle coordination. His work addresses critical challenges in real-world control applications, particularly when system dynamics are partially or fully unknown. In his highly cited 2023 paper, "Data-Driven Learning Extended State Observers for Nonlinear Systems," Lv introduces a novel framework that eliminates the need for prior model knowledge—such as nominal parameters—by combining data-driven learning with extended state observer (ESO) design. This approach enables accurate estimation of internal dynamics, external disturbances, and unknown control gains in first-order nonlinear systems, validated through hardware-in-the-loop simulations. The paper has already garnered 7 citations, reflecting its impact on advancing robust control theory. Lv also contributes to autonomous marine systems, as seen in his work on collision-free output-feedback super-twisting control for robotic surface vehicles, achieving coordinated path following with experimental validation (5 citations). His research bridges theoretical innovation and practical implementation, offering scalable solutions for safety-critical applications. Lv’s achievements position him as a key contributor to the next generation of intelligent, model-free control systems.
Research Focus
Key Achievements
Top Papers
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