Roujin Mousavifard
Papers
2
Total Citations
27
H-Index
2
About
Roujin Mousavifard is a rising researcher in aerial robotics and control systems, whose work focuses on advancing the autonomy and coordination of unmanned aerial vehicles (UAVs). Her key research areas include model predictive control (MPC), multi-agent formation control, and the integration of machine learning with classical control methods. Mousavifard’s most-cited paper, "Control of Aerial Robots Using Convex QP LMPC and Learning-Based Explicit-MPC" (2024, 23 citations), introduces a novel cascade control framework for quadrotors that combines linear MPC with convex quadratic programming and learning-based explicit control, significantly improving trajectory tracking performance. Her earlier work, "Formation Control of Multiple Aerial Robots Using LSTM-based Model Predictive Control" (2022, 4 citations), tackles the complex challenge of multi-UAV formation control by employing Long Short-Term Memory networks to handle nonlinear quadrotor dynamics without simplifying assumptions, yielding more reliable results. These contributions demonstrate her ability to bridge theoretical control design with practical implementation, making her work valuable for applications in surveillance, search-and-rescue, and autonomous drone swarms. Mousavifard’s research is gaining recognition for its innovative use of data-driven methods to enhance the robustness and efficiency of aerial robotic systems.
Research Focus
Key Achievements
Top Papers
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