Formal synthesis of analytic controllers for sampled-data systems via genetic programming

Cees F. Verdier; Manuel Mazo

doi:10.1109/CDC.2018.8619121

Formal synthesis of analytic controllers for sampled-data systems via genetic programming

Cees F. Verdier, Manuel Mazo

Team Tamas Keviczky

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

4 Citations (Scopus)

32 Downloads (Pure)

Abstract

This paper presents an automatic formal controller synthesis method for nonlinear sampled-data systems with safety and reachability specifications. Fundamentally, the presented method is not restricted to polynomial systems and controllers. We consider a periodically switched controllers based on a Control Lyapunov Barrier-like function. The proposed method utilizes genetic programming to synthesize these function in analytic form, as well as the controller modes. Correctness of the controller are subsequently verified by means of a Satisfiability Modulo Theories solver. Effectiveness of the proposed methodology is demonstrated on multiple systems.

Original language	English
Title of host publication	Proceedings of the 57th IEEE Conference on Decision and Control (CDC 2018)
Editors	Andrew R. Teel, Magnus Egerstedt
Place of Publication	Piscataway, NJ, USA
Publisher	IEEE
Pages	4896-4901
ISBN (Electronic)	978-1-5386-1395-5
DOIs	https://doi.org/10.1109/CDC.2018.8619121
Publication status	Published - 2018
Event	CDC 2018: 57th IEEE Conference on Decision and Control - Miami, United States Duration: 17 Dec 2018 → 19 Dec 2018

Conference

Conference	CDC 2018: 57th IEEE Conference on Decision and Control
Country/Territory	United States
City	Miami
Period	17/12/18 → 19/12/18

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care

Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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08619121Final published version, 519 KB

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@inproceedings{7963a66e09fd45648d66414f67d062fa,

title = "Formal synthesis of analytic controllers for sampled-data systems via genetic programming",

abstract = "This paper presents an automatic formal controller synthesis method for nonlinear sampled-data systems with safety and reachability specifications. Fundamentally, the presented method is not restricted to polynomial systems and controllers. We consider a periodically switched controllers based on a Control Lyapunov Barrier-like function. The proposed method utilizes genetic programming to synthesize these function in analytic form, as well as the controller modes. Correctness of the controller are subsequently verified by means of a Satisfiability Modulo Theories solver. Effectiveness of the proposed methodology is demonstrated on multiple systems.",

author = "Verdier, {Cees F.} and Manuel Mazo",

note = "Green Open Access added to TU Delft Institutional Repository {\textquoteleft}You share, we take care!{\textquoteright} – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; CDC 2018: 57th IEEE Conference on Decision and Control ; Conference date: 17-12-2018 Through 19-12-2018",

year = "2018",

doi = "10.1109/CDC.2018.8619121",

language = "English",

pages = "4896--4901",

editor = "Teel, {Andrew R. } and Egerstedt, {Magnus }",

booktitle = "Proceedings of the 57th IEEE Conference on Decision and Control (CDC 2018)",

publisher = "IEEE",

address = "United States",

}

Verdier, CF & Mazo, M 2018, Formal synthesis of analytic controllers for sampled-data systems via genetic programming. in AR Teel & M Egerstedt (eds), Proceedings of the 57th IEEE Conference on Decision and Control (CDC 2018). IEEE, Piscataway, NJ, USA, pp. 4896-4901, CDC 2018: 57th IEEE Conference on Decision and Control, Miami, United States, 17/12/18. https://doi.org/10.1109/CDC.2018.8619121

Formal synthesis of analytic controllers for sampled-data systems via genetic programming. / Verdier, Cees F.; Mazo, Manuel.
Proceedings of the 57th IEEE Conference on Decision and Control (CDC 2018). ed. / Andrew R. Teel; Magnus Egerstedt. Piscataway, NJ, USA: IEEE, 2018. p. 4896-4901.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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N2 - This paper presents an automatic formal controller synthesis method for nonlinear sampled-data systems with safety and reachability specifications. Fundamentally, the presented method is not restricted to polynomial systems and controllers. We consider a periodically switched controllers based on a Control Lyapunov Barrier-like function. The proposed method utilizes genetic programming to synthesize these function in analytic form, as well as the controller modes. Correctness of the controller are subsequently verified by means of a Satisfiability Modulo Theories solver. Effectiveness of the proposed methodology is demonstrated on multiple systems.

AB - This paper presents an automatic formal controller synthesis method for nonlinear sampled-data systems with safety and reachability specifications. Fundamentally, the presented method is not restricted to polynomial systems and controllers. We consider a periodically switched controllers based on a Control Lyapunov Barrier-like function. The proposed method utilizes genetic programming to synthesize these function in analytic form, as well as the controller modes. Correctness of the controller are subsequently verified by means of a Satisfiability Modulo Theories solver. Effectiveness of the proposed methodology is demonstrated on multiple systems.

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BT - Proceedings of the 57th IEEE Conference on Decision and Control (CDC 2018)

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T2 - CDC 2018: 57th IEEE Conference on Decision and Control

Y2 - 17 December 2018 through 19 December 2018

ER -

Formal synthesis of analytic controllers for sampled-data systems via genetic programming

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