Virtual Reference Feedback Tuning with Data-Driven Reference Model Selection
Abstract
In control applications where finding a model of the plant is the most costly and time consuming task, Virtual Reference Feedback Tuning (VRFT) represents a valid - purely data-driven - alternative for the design of model reference controllers. However, the selection of a proper reference model within a model-free setting is known to be a critical task, with this model typically playing the role of a hyper-parameter. In this work, we extend the VRFT methodology to compute both a proper reference model and the corresponding optimal controller parameters from data by means of Particle Swarm optimization. The effectiveness of the proposed approach is illustrated on a benchmark simulation example.
Cite
Text
Breschi and Formentin. "Virtual Reference Feedback Tuning with Data-Driven Reference Model Selection." Proceedings of the 2nd Conference on Learning for Dynamics and Control, 2020.Markdown
[Breschi and Formentin. "Virtual Reference Feedback Tuning with Data-Driven Reference Model Selection." Proceedings of the 2nd Conference on Learning for Dynamics and Control, 2020.](https://mlanthology.org/l4dc/2020/breschi2020l4dc-virtual/)BibTeX
@inproceedings{breschi2020l4dc-virtual,
title = {{Virtual Reference Feedback Tuning with Data-Driven Reference Model Selection}},
author = {Breschi, Valentina and Formentin, Simone},
booktitle = {Proceedings of the 2nd Conference on Learning for Dynamics and Control},
year = {2020},
pages = {37-45},
volume = {120},
url = {https://mlanthology.org/l4dc/2020/breschi2020l4dc-virtual/}
}