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Online System Identification for Online Feedback Optimization Controllers
We want to investigate online model identification for the novel class of Online Feedback Optimization Controllers. Furthermore, we will analyse the interaction between the identification algorithms and the Online Feedback Optimization Controllers.
Keywords: Feedback Optimization
Control Systems
Adaptive Control
System Identification
Online Feedback Optimization is a novel way of controlling a system. The goal of such a controller is not to track a reference point, but to drive the system to an optimal point defined by an optimization problem (e.g. maximum efficiency).
For such controllers some model information is needed. This information can either be derived offline or online.
In this project we are aiming at estimating and learning this model information online i.e., while the system is running. This means we are adapting our controller during the closed-loop operation.
This procedure is called adaptive control and established for "regular" controller that track a reference. For the novel Online Feedback Controllers this has not been done before.
Online Feedback Optimization is a novel way of controlling a system. The goal of such a controller is not to track a reference point, but to drive the system to an optimal point defined by an optimization problem (e.g. maximum efficiency). For such controllers some model information is needed. This information can either be derived offline or online. In this project we are aiming at estimating and learning this model information online i.e., while the system is running. This means we are adapting our controller during the closed-loop operation. This procedure is called adaptive control and established for "regular" controller that track a reference. For the novel Online Feedback Controllers this has not been done before.
The goal is to investigate online model identification for the novel class of Online Feedback Optimization Controllers. Furthermore, we will analyse the interaction between the identification algorithms and the Online Feedback Optimization Controllers.
The student will be given the time to read up on the adaptive control and system identification literature. Afterwards, these existing tools or new tools proposed by the student shall be used to obtain a model for the Online Feedback Optimization Controllers. Finally, we will investigate the resulting control structure of identification algorithm and Online Feedback Optimization Controller. This will include analyses of the performance, stability and further aspects.
If the results are promising they can be turned into a publication.
The goal is to investigate online model identification for the novel class of Online Feedback Optimization Controllers. Furthermore, we will analyse the interaction between the identification algorithms and the Online Feedback Optimization Controllers.
The student will be given the time to read up on the adaptive control and system identification literature. Afterwards, these existing tools or new tools proposed by the student shall be used to obtain a model for the Online Feedback Optimization Controllers. Finally, we will investigate the resulting control structure of identification algorithm and Online Feedback Optimization Controller. This will include analyses of the performance, stability and further aspects.
If the results are promising they can be turned into a publication.
Lukas Ortmann: ortmannl@control.ee.ethz.ch
Miguel Picallo Cruz: miguelp@control.ee.ethz.ch
Lukas Ortmann: ortmannl@control.ee.ethz.ch Miguel Picallo Cruz: miguelp@control.ee.ethz.ch