Mohammad Amin Najafqolian
Papers
2
Total Citations
27
H-Index
2
About
Mohammad Amin Najafqolian is a researcher advancing the frontiers of autonomous aerial robotics, with a focus on intelligent control systems for multi-agent UAVs. His work bridges model predictive control (MPC) and deep learning to solve complex trajectory and formation challenges. In his highly cited 2024 paper (23 citations), he introduced a novel cascade control framework for quadrotors that combines linear MPC with convex quadratic programming for position control and a nonlinear attitude controller, achieving superior trajectory tracking performance. His 2022 work on formation control (4 citations) further demonstrates his impact, proposing an LSTM-based MPC design that preserves the full nonlinear quadrotor dynamics without simplifying assumptions—yielding more reliable results for multi-agent coordination. By integrating learning-based methods with classical control theory, Najafqolian addresses critical gaps in real-time adaptability and scalability for aerial robot swarms. His contributions are particularly relevant for applications in search-and-rescue, surveillance, and autonomous logistics, where robust formation flight and precise trajectory control are essential. His work represents a significant step toward practical, intelligent aerial systems that can operate reliably in dynamic environments.
Research Focus
Key Achievements
Top Papers
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