Yasamin Raeisi
Papers
1
Total Citations
7
H-Index
1
About
Yasamin Raeisi is a researcher in robotics and control systems, with a focus on the trajectory tracking control of nonholonomic wheeled mobile robots. Her most-cited work, published in 2015, addresses the challenging problem of output feedback control for car-like drive robots under model uncertainties and without direct velocity measurement. By integrating radial basis function (RBF) neural networks with a linear observer, Raeisi developed a robust control framework that compensates for system uncertainties while ensuring stable tracking performance—a significant contribution to autonomous vehicle and mobile robot navigation. Though her citation count is modest, this foundational paper has garnered 7 citations, reflecting its relevance to researchers tackling similar control challenges. Raeisi’s work demonstrates a practical approach to bridging neural network-based learning with classical control theory, offering valuable insights for students and engineers working on real-world robotic systems where sensor limitations and dynamic uncertainties are prevalent. Her contributions highlight the ongoing need for adaptive, observer-based control strategies in nonholonomic robotics.
Research Focus
Key Achievements
Top Papers
- 1