Virtual Reference Feedback Tuning with data-driven reference model selection

Valentina Breschi, Simone Formentin
Proceedings of the 2nd Conference on Learning for Dynamics and Control, PMLR 120:37-45, 2020.

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 this Paper

BibTeX
@InProceedings{pmlr-v120-breschi20a, 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}, pages = {37--45}, year = {2020}, editor = {Bayen, Alexandre M. and Jadbabaie, Ali and Pappas, George and Parrilo, Pablo A. and Recht, Benjamin and Tomlin, Claire and Zeilinger, Melanie}, volume = {120}, series = {Proceedings of Machine Learning Research}, month = {10--11 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v120/breschi20a/breschi20a.pdf}, url = {https://proceedings.mlr.press/v120/breschi20a.html}, 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.} }
Endnote
%0 Conference Paper %T Virtual Reference Feedback Tuning with data-driven reference model selection %A Valentina Breschi %A Simone Formentin %B Proceedings of the 2nd Conference on Learning for Dynamics and Control %C Proceedings of Machine Learning Research %D 2020 %E Alexandre M. Bayen %E Ali Jadbabaie %E George Pappas %E Pablo A. Parrilo %E Benjamin Recht %E Claire Tomlin %E Melanie Zeilinger %F pmlr-v120-breschi20a %I PMLR %P 37--45 %U https://proceedings.mlr.press/v120/breschi20a.html %V 120 %X 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.
APA
Breschi, V. & Formentin, S.. (2020). Virtual Reference Feedback Tuning with data-driven reference model selection. Proceedings of the 2nd Conference on Learning for Dynamics and Control, in Proceedings of Machine Learning Research 120:37-45 Available from https://proceedings.mlr.press/v120/breschi20a.html.

Related Material