Moein Sarbandi

I am an MSCA doctoral researcher in Automatic Control at the Laboratory of Digital Sciences of Nantes (LS2N) (CODEx group), École Centrale Nantes, funded by the DENSE Doctoral Network under Horizon Europe. My PhD research is supervised by Prof. Franck Plestan and Dr. Mohamed Assaad Hamida.

My work develops robust, data-driven, and learning-enhanced control methods for uncertain nonlinear systems, with a primary application to floating offshore wind turbines. I combine adaptive sliding-mode design, neural-network-augmented control, data-driven methods, and nonlinear observers, with validation in MATLAB/Simulink, OpenFAST, and experimental software-in-the-loop platforms.

Research interests

• Adaptive and robust nonlinear control: integral sliding-mode control, super-twisting algorithms, adaptive-gain methods, and CLF-QP
• Data-driven control: Willems’ Fundamental Lemma, robust informativity using QMI/S-lemma tools, Koopman-operator methods, and DeePC
• Learning-based control: online neural networks integrated with classical sliding-mode designs
• Predictive control: model predictive control and data-enabled predictive control
• Nonlinear observers: second-order sliding-mode observers for state, disturbance, wind-speed, and aerodynamic-parameter estimation
• Applications: renewable-energy systems, especially floating offshore wind turbines

Research mobility and collaboration

I completed a research secondment at BTU Cottbus-Senftenberg with Prof. Johannes Schiffer’s group, focusing on data-driven control within the DENSE network. I also collaborate with Prof. Antonella Ferrara’s group at the University of Pavia.

Academic service

I have completed more than 30 journal reviews for Automatica, IEEE Transactions on Cybernetics, ISA Transactions, Wind Engineering, and Energy Reports. I have also reviewed submissions for the IFAC World Congress 2026 and the American Control Conference 2026.

My publications and verified researcher record are available through ORCID and the links below. I welcome research collaborations in nonlinear control, data-driven methods, wind energy, robotics, and autonomous systems.