In this project data-driven controller tuning is considered. In this approach, the controller is designed without the use of a model. Instead, a control objective is minimized directly using the data. An advantage of such direct methods is that the order of the controller can be fixed, in contrast to many model based methods where the order of the controller depends on the order of the model. Furthermore, the problem of undermodeling is omitted since no plant model is used.
Iterative approaches as Iterative Feedback Tuning (IFT) and Iterative Correlation Based Tuning (ICbT) as well as the non-iterative Virtual Reference Feedback Tuning (VRFT) have shown to be effective in practice. However, they all suffer from the same drawback; closed-loop stability can in general not be guaranteed and since no model is available the well-known robustness margins cannot be evaluated.
This project focusses on the stability question in a non-iterative approach. A stability condition that can be implemented in the controller design step is proposed. Furthermore the effect of measurement noise is studied and the correlation approach is used to reduce it.
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