Fethi Bencherki
About me
PhD Student at the Department of Automatic Control since Aug 2021. Supervised by Anders Rantzer. I have a MSc in Control Systems where I did my thesis on state-space identification of switched systems. The interested reader can find it here.
Teaching
Fall 2021
- FRTN05 - Non-Linear Control and Servo Systems
Spring 2022
- FRTF05 - Automatic control, basic course for FIPi
Fall 2022
- FRTF05 - Automatic Control, Basic Course for DE
- FRTN05 - Non-Linear Control and Servo Systems
Fall 2023
- FRTF05 - Automatic Control, Basic Course for DE
- FRTN05 - Non-Linear Control and Servo Systems
Research and Publications
I am within the NEST project and my research interest revolves around developing scalable control approaches for large scale networks. I am also interested in learning-based control and the identification of switched systems. Below is a list of publications and preprints.
Preprints
Observer based switched-linear system identification, submitted to Nonlinear Analysis: Hybrid Systems
MIMO switched-linear system identification from input-output data, submitted to Signal Processing
Publications
Robust adaptive data-driven control of positive systems with application to learning in SSP problems, 7th Annual Learning for Dynamics & Control Conference (L4DC)
Data-driven adaptive dispatching policies for processing networks, IEEE Control Systems Letters & The American Control Conference (ACC 2025)
MIMO-SLS identification from input-output data, 18th IEEE International Conference on Control & Automation (ICCA 2024)
Realization of MIMO-SLSs from Markov parameters via forward/backward corrections, European Control Conference (ECC 2024)
Robust simultaneous stabilization via minimax adaptive control, 62nd IEEE Conference on Decision and Control (CDC 2023)
Basis transform in switched linear system state-space models from input-output data, International Journal of Adaptive Control and Signal Processing
Realization of multi-input/multi-output switched linear systems from Markov parameters, Nonlinear Analysis: Hybrid Systems
Achieving consensus in networks of increasingly stubborn voters, 61st IEEE Conference on Decision and Control (CDC 2022)
