Yasmine Marani

About

Yasmine is a Ph.D. candidate at The Robotics, Intelligent Systems and Control (RISC) Lab under the supervision of Prof. Eric Feron and Prof. Taous-Meriem Laleg-Kirati. Prior to joining KAUST as a Ph.d. student, she was an intern in the EMANG research group where she worked on the fouling estimation and localization in the Direct Contact Membrane Distillation (DCMD) process. Her current research focuses on developing hybrid  model-based and learning-based estimation algorithms for diverse classes of nonlinear systems.   

PhD research topic 

''Learning-based observer design for nonlinear systems ''

The main objective of Yasmine's research is to develop estimation algorithms for nonlinear systems to reconstruct their internal variables by leveraging the reliability and rigor of model-based approaches with the power of artificial intelligence.  Indeed, the variables of several systems, such as ground robots and drones are only partially accessible, but their knowledge is crucial for monitoring and control design, which motivates the need to design estimation algorithms with theoretical guarantees for different classes of systems.

Currently Yasmine is working simultaneously on both state-of-the-art UAV problems and the theoretical aspect of learning-based observer design. More precisely, she is working on controlling drones in non-inertial reference frames, and on learning-based observers for uncertain nonlinear systems and systems subject to measurement delay.  

Selected Publications 

•  Y. Marani, I. N’Doye, and T. M. Laleg Kirati, “Non-asymptotic neural network-based state and disturbance estimation for a class of nonlinear systems using modulating functions,” in 2023 American Control Conference (ACC), pp. 3062–3068, 2023.

• Y. Marani, I. N’Doye, and T. M. Laleg-Kirati, “Deep-learning based design of cascade observers for discrete-time nonlinear systems with output delay,” IFAC-PapersOnLine, vol. 56, no. 2, pp. 9869–9874, 2023. 22nd IFAC World Congress.

• Y. Marani, K. Telegenov, E. Feron, and M.-T. L. Kirati, “Drone reference tracking in a non-inertial frame using sliding mode control based Kalman filter with unknown input,” in 2022 IEEE Conference on Control Technology and Applications (CCTA), pp. 9–16, 2022.

• Y. Marani, K. Telegenov, E. Feron, and M.-T. L. Kirati, “Drone reference tracking in a non-inertial frame: control, design, and experiment,” in 2022 IEEE/AIAA 41st Digital Avionics Systems Conference (DASC),pp. 1–8, 2022.

• Y. Marani, E. Feron, and M.-T. L. Kirati, “Observer-based control of an unmanned aerial vehicle in a non-inertial reference frame,” in 2024 IEEE Conference on Control Technology and Applications (CCTA), 2024.