Meysam Yadegar
Papers
3
Total Citations
32
H-Index
2
About
Meysam Yadegar is a researcher at the forefront of intelligent control systems, robotics, and nonlinear dynamics, whose work bridges the gap between theoretical advances and practical automation challenges. His most impactful contribution, a 2022 paper on data-driven sensor fault detection and isolation (23 citations), introduces a novel deep neural-network Koopman operator framework. This approach enables the construction of linear predictors for complex nonlinear systems, offering a powerful tool for real-time monitoring and safety in autonomous platforms. In the realm of robotics, Yadegar has made significant strides in autonomous target docking for non-holonomic mobile robots (2024, 7 citations), addressing the industrial need for robust, low-cost, and precise latching maneuvers while incorporating obstacle avoidance and final velocity control. His earlier work on a proportional navigation-based method for robotic interception planning (2021) adapts aerospace guidance laws to enable smooth, controlled grasps between robots. With a total of over 30 citations across his key publications, Yadegar’s research is shaping the future of autonomous systems, from fault-resilient control to seamless robot-robot interaction, making him a notable figure in modern robotics and control theory.
Research Focus
Key Achievements
Top Papers
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