Advanced Data-driven Controller Synthesis
- ID: 14371
- Type(s): Projet de semestre MA GM
- Section(s): GM
- Status: Validé
- Professor: Alireza Karimi, Vaibhav Gupta
The goal of this project is to expand the data-driven controller synthesis framework developed at the lab to address performance requirements that are not adequately captured by the objectives currently available. At present, the framework supports (H_2) and (H_\infty) objectives and constraints, as well as robustness specifications based on Integral Quadratic Constraints (IQCs). However, practical requirements such as actuator saturation limits may require alternative performance measures and synthesis formulations.
The project will focus on the following components:
* Theoretical: Investigate alternative control objectives, such as (H_1) performance and regret-based criteria, and develop corresponding data-driven controller synthesis methods.
* Software: Implement the developed algorithms in the lab’s `datadriven` software library, with an emphasis on numerical reliability and integration with the existing framework.
* Experimental: Validate the proposed methods on real-world experimental systems available at the lab, with particular attention to their ability to handle saturation-related constraints and performance requirements.
- Professor(s)
- Alireza Karimi, Vaibhav Gupta
- Administration
- Barbara Marie-Louise Frédérique Schenkel
- Site
- ddmac.epfl.ch, la.epfl.ch
Controller Synthesis for Adaptive Optics Systems in Telescopes
- ID: 14124
- Type(s): Projet de semestre MA GM, Projet de Master (PDM) GM
- Section(s): GM
- Status: Complet
- Professor: Alireza Karimi, Vaibhav Gupta
ROJECT SUMMARY
Adaptive optics (AO) systems are critical in overcoming atmospheric distortions in ground-based telescopes, enabling sharper and more detailed astronomical observations. This project focuses on designing and synthesising a robust, real-time controller for an AO system. By leveraging advanced control theory, the project aims to significantly enhance the performance of AO systems, ensuring reliable correction of wavefront distortions and improving the quality of astronomical images.
BACKGROUND AND MOTIVATION
Telescopes observing through Earth’s atmosphere suffer from distortions caused by turbulent air layers, which degrade image resolution. Adaptive optics counteract this by using deformable mirrors and wavefront sensors to correct for distortions in real-time.
Current AO systems, while effective, face limitations in:
* Handling dynamic, unknown atmospheric conditions.
* Scalability for large telescopes and next-generation systems.
By synthesising a controller tailored to these challenges, this project seeks to push the boundaries of AO performance, paving the way for discoveries in astronomy and astrophysics.
OBJECTIVES
1. Design and develop a robust controller that addresses dynamic atmospheric variations.
2. Validate performance through simulation (and hardware) implementation using an AO testbed.
3. Ensure scalability and adaptability of the controller for different telescopes and operational conditions.
REQUIREMENTS
The project demands a solid academic foundation in control courses, particularly in ‘Advanced Control Systems.’ Proficiency in the frequency domain approach and robust control techniques is especially critical.
- Professor(s)
- Alireza Karimi, Vaibhav Gupta
- Administration
- Barbara Marie-Louise Frédérique Schenkel
- External
- Department of Astronomy, UNIGE, Isaac Dinis, [email protected]
- Site
- ddmac.epfl.ch, la.epfl.ch
Data-driven infinite horizon distributionally robust control
- ID: 14377
- Type(s): Projet de semestre MA GM
- Section(s): GM
- Status: Validé
- Professor: Alireza Karimi, Vaibhav Gupta
The goal of this project is to expand the data-driven controller synthesis framework developed at the lab to address performance requirements that are not adequately captured by the objectives currently available. At present, the framework supports (H_2) and (H_\infty) objectives and constraints, as well as robustness specifications based on Integral Quadratic Constraints (IQCs).
The project will focus on the following components:
* Theoretical: Investigate alternative control objectives, in particular Distributionally Robust Controls (DRO), and develop corresponding data-driven controller synthesis methods.
* Software: Implement the developed algorithms in the lab’s `datadriven` software library, with an emphasis on numerical reliability and integration with the existing framework.
* Experimental: Validate the proposed methods on real-world experimental systems available at the lab, with particular attention to their ability to handle saturation-related constraints and performance requirements.
- Professor(s)
- Alireza Karimi, Vaibhav Gupta
- Administration
- Barbara Marie-Louise Frédérique Schenkel
- Site
- ddmac.epfl.ch, la.epfl.ch
Data-driven swing up controller of an inverted pendulum using partial feedback linearization
- ID: 14246
- Type(s): Projet de semestre MA GM
- Section(s): GM
- Status: Validé
- Professor: Alireza Karimi, Vishnu Varadan
This project explores the implementation of a partial feedback linearization algorithm for the swing up control of an inverted pendulum. The algorithm will also be extended to the data-driven paradigm. The designed controller will be implemented on the hardware setup using LabVIEW.
- Professor(s)
- Alireza Karimi, Vishnu Varadan
- Administration
- Barbara Marie-Louise Frédérique Schenkel
- Site
- https://www.epfl.ch/labs/la/, https://www.epfl.ch/labs/ddmac/
Robust controller design and implementation on an Active Suspension system
- ID: 14245
- Type(s): Projet de semestre MA GM
- Section(s): GM
- Status: Validé
- Professor: Alireza Karimi, Vishnu Varadan
The objective of this project is to set up an Active Suspension system test bench and apply data-driven adaptive control techniques on it. Data acquisition and controller implementation would be done using LabVIEW and NI DAQs. The designed controller should be robust to the parametric uncertainties in the system and will be verified and validated on the test bench.
- Professor(s)
- Alireza Karimi, Vishnu Varadan
- Administration
- Barbara Marie-Louise Frédérique Schenkel
- Site
- https://www.epfl.ch/labs/la/, https://www.epfl.ch/labs/ddmac/