Alireza Karimi
EPFL STI IGM SCI-STI-AK
ME C2 407 (Bâtiment ME)
Station 9
1015 Lausanne
+41 21 693 59 25
Office:
ME C2 407
EPFL
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SCI-STI-AK
Web site: Web site: https://www.epfl.ch/labs/ddmac/
Fields of expertise
System identification, robust control, data-driven controller tuning, Mechatronics, Control of electrical grids
Academic activities
- Member of doctoral program in Robotics, Control and Intelligent Systems.- Member of teaching committee of Mechanical Engineering Section.
- Adviser of Master students in Control and Mechatronics orientation.
International Activities
- Associate Editor of European Journal of Control (2004-2013).- Member of IFAC Technical Committee on Linear Systems and Robust Control.
- Member of IEEE-CSS Technical Committee on System Identification and Adaptive Control.
- Member of Award Selection Committee of IFAC World Congress 2011.
- IPC member of several international conferences (CIFA 2006, IEEE-MED 2008, CIFA 2012, IFAC-PID 2012, IFAC-SSSC 2013, IFAC-ACE 2016, IFAC-SSSC 2016, IFAC-WC 2017).
Biography
Alireza Karimi received his B. Sc. and M. Sc. degrees in Electrical Engineering in 1987 and 1990 from Amir Kabir University (Tehran Polytechnic). After 3 years of industrial experience he joined Institut National Polytechnique de Grenoble (INPG) in France and received his DEA and Ph. D. degrees both on Automatic Control in 1994 and 1997, respectively. He was Assistant Professor at Electrical Engineering Department of Sharif University of Technology in Teheran from 1998 to 2000. He is currently Professor of Automatic Control in the Institute of Mechanical Engineering of EPFL. He was an Associate Editor of European Journal of Control from 2004 to 2013. His research interests include closed-loop identification, data-driven controller tuning approaches and robust control with application on mechatronic systems and electrical grids.Publications
Other publications
List of publications
All PublicationsRecent Journal Papers
Citation records
Citations from Google ScholarResearchgate profile
Recently published book
Adaptive Control: Algorithms, Analysis and Applications by Landau, I.D., Lozano, R., MResearch
Data-Driven Modelling and Control
Time-Domain Data-Driven MethodsFrequency-Domain Data-Driven Methods
Distributed Control of Smart Grids
Control Applications
International Benchmarks
- Rejection of unknown/time-varying multiple narrow band disturbances (2013)- Design and Optimisation of Restricted Complexity Controllers (2003)
- Flexible Transmission Benchmark (1995)
Frequency-Domain Robust Controller Design Toolbox
The Toolbox can be downloaded for free from this website.Teaching & PhD
Teaching
Mechanical Engineering
PhD Students
Eyuboglu Mert, Gupta Vaibhav, Klauser Elias Sebastian, Mehdikhani Sarvjahany Mahdi, Qin Zhaoming, Varadan Vishnu,Past EPFL PhD Students
Asmari Saadabad Navid , Butcher Mark Edward John , Emedi Zlatko , Galdos Sanz de Galdeano Gorka , Kammer Christoph Manuel , Kunze Marc , Madani Seyed Sohail , Nicoletti Achille , Sad Abadi Mahdieh Sadat , Schuchert Philippe Louis , van Heusden Klaske ,Teaching doctoral courses in the international graduate school on control - Identification in closed-loop operation and controller reduction (May, 2015) - Adaptive Control: From needs to applications (May, 2014)
Courses
Control systems and discrete-time control
This course includes modelling and analysis of dynamic systems, basic principles and analysis of feedback control systems, controller design in frequency domain (loop shaping) and in state space (linear quadratic regulator), discrete-time systems and digital controller design (polynomial approach).
System identification
Models (classifications, representations). Excitation signals (impulse, step, random, pseudo random). Least Squares algorithm (linear regression, analysis in stochastic case, bias-variance tradeoff). Time-domain nonparametric identification methods (impulse response by the correlation approach). Frequency-domain nonparametric identification methods based on the Fourier and spectral analysis. Pa
Advanced control systems
This course covers some theoretical and practical aspects of robust and adaptive control. This includes H-2 and H-infinity control in model-based and data-driven framework by convex optimization, direct, indirect and switching adaptive control. The methods are implemented in a hands-on lab.