Data-driven design of switching reference governors for brake-by-wire applications

Andrea Sassella, Valentina Breschi, Simone Formentin

Proceedings of the 3rd Conference on Learning for Dynamics and Control, PMLR 144:99-110, 2021.

Abstract

Nowadays, data are ubiquitous in control design and data-driven approaches are in constant evolution. By following such a trend, in this paper we propose an approach for the direct data-driven design of switching reference governors for nonlinear plants and we apply it within a brake-by-wire application. The braking system is assumed to be pre-stabilized via a simple unknown controller attaining unsatisfactory performance in terms of output tracking and actuator effort. Hence, the reference governor is used to improve the overall closed-loop behavior, resulting into safer maneuvering. Preliminary results on a simulation setup show the effectiveness of the proposed strategy, thus motivating further investigation on the topic.

Cite this Paper

BibTeX


@InProceedings{pmlr-v144-sassella21a,
  title = 	 {Data-driven design of switching reference governors for brake-by-wire applications},
  author =       {Sassella, Andrea and Breschi, Valentina and Formentin, Simone},
  booktitle = 	 {Proceedings of the 3rd Conference on Learning for Dynamics and Control},
  pages = 	 {99--110},
  year = 	 {2021},
  editor = 	 {Jadbabaie, Ali and Lygeros, John and Pappas, George J. and A. Parrilo, Pablo and Recht, Benjamin and Tomlin, Claire J. and Zeilinger, Melanie N.},
  volume = 	 {144},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {07 -- 08 June},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v144/sassella21a/sassella21a.pdf},
  url = 	 {https://proceedings.mlr.press/v144/sassella21a.html},
  abstract = 	 {Nowadays, data are ubiquitous in control design and data-driven approaches are in constant evolution. By following such a trend, in this paper we propose an approach for the direct data-driven design of switching reference governors for nonlinear plants and we apply it within a brake-by-wire application. The braking system is assumed to be pre-stabilized via a simple unknown controller attaining unsatisfactory performance in terms of output tracking and actuator effort. Hence, the reference governor is used to improve the overall closed-loop behavior, resulting into safer maneuvering. Preliminary results on a simulation setup show the effectiveness of the proposed strategy, thus motivating further investigation on the topic.}
}

Endnote

%0 Conference Paper
%T Data-driven design of switching reference governors for brake-by-wire applications
%A Andrea Sassella
%A Valentina Breschi
%A Simone Formentin
%B Proceedings of the 3rd Conference on Learning for Dynamics and Control
%C Proceedings of Machine Learning Research
%D 2021
%E Ali Jadbabaie
%E John Lygeros
%E George J. Pappas
%E Pablo A. Parrilo
%E Benjamin Recht
%E Claire J. Tomlin
%E Melanie N. Zeilinger	
%F pmlr-v144-sassella21a
%I PMLR
%P 99--110
%U https://proceedings.mlr.press/v144/sassella21a.html
%V 144
%X Nowadays, data are ubiquitous in control design and data-driven approaches are in constant evolution. By following such a trend, in this paper we propose an approach for the direct data-driven design of switching reference governors for nonlinear plants and we apply it within a brake-by-wire application. The braking system is assumed to be pre-stabilized via a simple unknown controller attaining unsatisfactory performance in terms of output tracking and actuator effort. Hence, the reference governor is used to improve the overall closed-loop behavior, resulting into safer maneuvering. Preliminary results on a simulation setup show the effectiveness of the proposed strategy, thus motivating further investigation on the topic.

APA


Sassella, A., Breschi, V. & Formentin, S.. (2021). Data-driven design of switching reference governors for brake-by-wire applications. Proceedings of the 3rd Conference on Learning for Dynamics and Control, in Proceedings of Machine Learning Research 144:99-110 Available from https://proceedings.mlr.press/v144/sassella21a.html.

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