Papers
6
Total Citations
207
H-Index
5
About
Alina Eqtami is a leading researcher in the intersection of model predictive control (MPC), visual servoing, and autonomous robotics, with a particular focus on resource-constrained and under-actuated systems. Her most significant contribution is the pioneering integration of self-triggered control mechanisms with nonlinear MPC, a framework that dramatically reduces computational and communication loads by determining the optimal time for the next control update rather than relying on periodic sampling. This innovation is critical for extending the autonomy and energy efficiency of small, fast, or underwater robots. Her work on self-triggered visual servoing for autonomous underwater vehicles (AUVs) and small autonomous robots is particularly notable, addressing the high computational cost of real-time visual tracking. Her most cited paper, "Self-triggered Model Predictive Control for nonholonomic systems" (2013), has garnered 68 citations, establishing a foundational approach in the field. Across her top-cited works, she has accumulated over 200 citations, demonstrating substantial impact. Eqtami’s research also extends to the challenging domain of medical microrobotics, where she has explored stabilizing the relative position of millirobots inside an MRI scanner, showcasing the breadth of her expertise in applying advanced control theory to real-world robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Self-triggered Model Predictive Control for nonholonomic systems68 citations · 2013
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