Data-Driven Controller Synthesis of Unknown Nonlinear Polynomial Systems via Control Barrier Certificates

Ameneh Nejati, Bingzhuo Zhong, Marco Caccamo, Majid Zamani
Proceedings of The 4th Annual Learning for Dynamics and Control Conference, PMLR 168:763-776, 2022.

Abstract

In this work, we propose a data-driven approach to synthesize safety controllers for continuous-time nonlinear polynomial-type systems with unknown dynamics. The proposed framework is based on notions of so-called control barrier certificates, constructed from data while providing a guaranteed confidence of 1 on the safety of unknown systems. Under a certain rank condition, we synthesize polynomial state-feedback controllers to ensure the safety of the unknown system only via a single trajectory collected from it. We demonstrate the effectiveness of our proposed results by applying them to a nonlinear polynomial-type system with unknown dynamics.

Cite this Paper

BibTeX
@InProceedings{pmlr-v168-nejati22a, title = {Data-Driven Controller Synthesis of Unknown Nonlinear Polynomial Systems via Control Barrier Certificates}, author = {Nejati, Ameneh and Zhong, Bingzhuo and Caccamo, Marco and Zamani, Majid}, booktitle = {Proceedings of The 4th Annual Learning for Dynamics and Control Conference}, pages = {763--776}, year = {2022}, editor = {Firoozi, Roya and Mehr, Negar and Yel, Esen and Antonova, Rika and Bohg, Jeannette and Schwager, Mac and Kochenderfer, Mykel}, volume = {168}, series = {Proceedings of Machine Learning Research}, month = {23--24 Jun}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v168/nejati22a/nejati22a.pdf}, url = {https://proceedings.mlr.press/v168/nejati22a.html}, abstract = {In this work, we propose a data-driven approach to synthesize safety controllers for continuous-time nonlinear polynomial-type systems with unknown dynamics. The proposed framework is based on notions of so-called control barrier certificates, constructed from data while providing a guaranteed confidence of 1 on the safety of unknown systems. Under a certain rank condition, we synthesize polynomial state-feedback controllers to ensure the safety of the unknown system only via a single trajectory collected from it. We demonstrate the effectiveness of our proposed results by applying them to a nonlinear polynomial-type system with unknown dynamics.} }
Endnote
%0 Conference Paper %T Data-Driven Controller Synthesis of Unknown Nonlinear Polynomial Systems via Control Barrier Certificates %A Ameneh Nejati %A Bingzhuo Zhong %A Marco Caccamo %A Majid Zamani %B Proceedings of The 4th Annual Learning for Dynamics and Control Conference %C Proceedings of Machine Learning Research %D 2022 %E Roya Firoozi %E Negar Mehr %E Esen Yel %E Rika Antonova %E Jeannette Bohg %E Mac Schwager %E Mykel Kochenderfer %F pmlr-v168-nejati22a %I PMLR %P 763--776 %U https://proceedings.mlr.press/v168/nejati22a.html %V 168 %X In this work, we propose a data-driven approach to synthesize safety controllers for continuous-time nonlinear polynomial-type systems with unknown dynamics. The proposed framework is based on notions of so-called control barrier certificates, constructed from data while providing a guaranteed confidence of 1 on the safety of unknown systems. Under a certain rank condition, we synthesize polynomial state-feedback controllers to ensure the safety of the unknown system only via a single trajectory collected from it. We demonstrate the effectiveness of our proposed results by applying them to a nonlinear polynomial-type system with unknown dynamics.
APA
Nejati, A., Zhong, B., Caccamo, M. & Zamani, M.. (2022). Data-Driven Controller Synthesis of Unknown Nonlinear Polynomial Systems via Control Barrier Certificates. Proceedings of The 4th Annual Learning for Dynamics and Control Conference, in Proceedings of Machine Learning Research 168:763-776 Available from https://proceedings.mlr.press/v168/nejati22a.html.

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