Systematic Synthesis of Passive Fault-Tolerant Augmented Neural Lyapunov Control Laws for Nonlinear Systems

Davide Grande; Davide Fenucci; Andrea Peruffo; Enrico Anderlini; Alexander B. Phillips; Giles Thomas; Georgios Salavasidis

doi:10.1109/CDC49753.2023.10383378

Systematic Synthesis of Passive Fault-Tolerant Augmented Neural Lyapunov Control Laws for Nonlinear Systems

Davide Grande^*, Davide Fenucci, Andrea Peruffo, Enrico Anderlini, Alexander B. Phillips, Giles Thomas, Georgios Salavasidis

^*Corresponding author for this work

Team Manuel Mazo Jr

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

3 Citations (Scopus)

7 Downloads (Pure)

Abstract

Performance and closed-loop stability of control systems can be jeopardised by actuator faults. Actuator redundancy in combination with appropriate control laws can increase the resiliency of a system to both loss of efficiency or jamming. Passive Fault-Tolerant Control (FTC) systems aim at designing a unique control law with guaranteed stability in both nominal and faulty scenarios. In this work, a novel machine learning-based method is devised to systematically synthesise control laws for systems affected by actuator faults, whilst formally certifying the closed-loop stability. The learning architecture trains two Artificial Neural Networks, one representing the control law, and the other resembling a Control Lyapunov Function (CLF). In parallel, a Satisfiability Modulo Theory solver is employed to certify that the obtained CLF formally guarantees the Lyapunov conditions. The method is showcased for two scenarios, one encompassing the stabilisation of an inverted pendulum with redundant actuators, whilst the other covers the control of an Autonomous Underwater Vehicle. The framework is shown capable of synthesising both linear and nonlinear control laws with minimal hyperparameter tuning and within limited computational resources.

Original language	English
Title of host publication	Proceedings of the 62nd IEEE Conference on Decision and Control (CDC 2023)
Publisher	IEEE
Pages	5851-5856
Number of pages	6
ISBN (Electronic)	979-8-3503-0124-3
DOIs	https://doi.org/10.1109/CDC49753.2023.10383378
Publication status	Published - 2023
Event	62nd IEEE Conference on Decision and Control, CDC 2023 - Singapore, Singapore Duration: 13 Dec 2023 → 15 Dec 2023

Publication series

Name	Proceedings of the IEEE Conference on Decision and Control
ISSN (Print)	0743-1546
ISSN (Electronic)	2576-2370

Conference

Conference	62nd IEEE Conference on Decision and Control, CDC 2023
Country/Territory	Singapore
City	Singapore
Period	13/12/23 → 15/12/23

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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Systematic_Synthesis_of_Passive_Fault-Tolerant_Augmented_Neural_Lyapunov_Control_Laws_for_Nonlinear_SystemsFinal published version, 1.2 MB

Cite this

Grande, D., Fenucci, D., Peruffo, A., Anderlini, E., Phillips, A. B., Thomas, G., & Salavasidis, G. (2023). Systematic Synthesis of Passive Fault-Tolerant Augmented Neural Lyapunov Control Laws for Nonlinear Systems. In Proceedings of the 62nd IEEE Conference on Decision and Control (CDC 2023) (pp. 5851-5856). (Proceedings of the IEEE Conference on Decision and Control). IEEE. https://doi.org/10.1109/CDC49753.2023.10383378

@inproceedings{3a052f4375e648eaa51fb87cc70126af,

title = "Systematic Synthesis of Passive Fault-Tolerant Augmented Neural Lyapunov Control Laws for Nonlinear Systems",

abstract = "Performance and closed-loop stability of control systems can be jeopardised by actuator faults. Actuator redundancy in combination with appropriate control laws can increase the resiliency of a system to both loss of efficiency or jamming. Passive Fault-Tolerant Control (FTC) systems aim at designing a unique control law with guaranteed stability in both nominal and faulty scenarios. In this work, a novel machine learning-based method is devised to systematically synthesise control laws for systems affected by actuator faults, whilst formally certifying the closed-loop stability. The learning architecture trains two Artificial Neural Networks, one representing the control law, and the other resembling a Control Lyapunov Function (CLF). In parallel, a Satisfiability Modulo Theory solver is employed to certify that the obtained CLF formally guarantees the Lyapunov conditions. The method is showcased for two scenarios, one encompassing the stabilisation of an inverted pendulum with redundant actuators, whilst the other covers the control of an Autonomous Underwater Vehicle. The framework is shown capable of synthesising both linear and nonlinear control laws with minimal hyperparameter tuning and within limited computational resources.",

author = "Davide Grande and Davide Fenucci and Andrea Peruffo and Enrico Anderlini and Phillips, {Alexander B.} and Giles Thomas and Georgios Salavasidis",

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.; 62nd IEEE Conference on Decision and Control, CDC 2023 ; Conference date: 13-12-2023 Through 15-12-2023",

year = "2023",

doi = "10.1109/CDC49753.2023.10383378",

language = "English",

series = "Proceedings of the IEEE Conference on Decision and Control",

publisher = "IEEE",

pages = "5851--5856",

booktitle = "Proceedings of the 62nd IEEE Conference on Decision and Control (CDC 2023)",

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Grande, D, Fenucci, D, Peruffo, A, Anderlini, E, Phillips, AB, Thomas, G & Salavasidis, G 2023, Systematic Synthesis of Passive Fault-Tolerant Augmented Neural Lyapunov Control Laws for Nonlinear Systems. in Proceedings of the 62nd IEEE Conference on Decision and Control (CDC 2023). Proceedings of the IEEE Conference on Decision and Control, IEEE, pp. 5851-5856, 62nd IEEE Conference on Decision and Control, CDC 2023, Singapore, Singapore, 13/12/23. https://doi.org/10.1109/CDC49753.2023.10383378

Systematic Synthesis of Passive Fault-Tolerant Augmented Neural Lyapunov Control Laws for Nonlinear Systems. / Grande, Davide; Fenucci, Davide; Peruffo, Andrea et al.
Proceedings of the 62nd IEEE Conference on Decision and Control (CDC 2023). IEEE, 2023. p. 5851-5856 (Proceedings of the IEEE Conference on Decision and Control).

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

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AU - Grande, Davide

AU - Fenucci, Davide

AU - Peruffo, Andrea

AU - Anderlini, Enrico

AU - Phillips, Alexander B.

AU - Thomas, Giles

AU - Salavasidis, Georgios

N1 - 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.

PY - 2023

Y1 - 2023

N2 - Performance and closed-loop stability of control systems can be jeopardised by actuator faults. Actuator redundancy in combination with appropriate control laws can increase the resiliency of a system to both loss of efficiency or jamming. Passive Fault-Tolerant Control (FTC) systems aim at designing a unique control law with guaranteed stability in both nominal and faulty scenarios. In this work, a novel machine learning-based method is devised to systematically synthesise control laws for systems affected by actuator faults, whilst formally certifying the closed-loop stability. The learning architecture trains two Artificial Neural Networks, one representing the control law, and the other resembling a Control Lyapunov Function (CLF). In parallel, a Satisfiability Modulo Theory solver is employed to certify that the obtained CLF formally guarantees the Lyapunov conditions. The method is showcased for two scenarios, one encompassing the stabilisation of an inverted pendulum with redundant actuators, whilst the other covers the control of an Autonomous Underwater Vehicle. The framework is shown capable of synthesising both linear and nonlinear control laws with minimal hyperparameter tuning and within limited computational resources.

AB - Performance and closed-loop stability of control systems can be jeopardised by actuator faults. Actuator redundancy in combination with appropriate control laws can increase the resiliency of a system to both loss of efficiency or jamming. Passive Fault-Tolerant Control (FTC) systems aim at designing a unique control law with guaranteed stability in both nominal and faulty scenarios. In this work, a novel machine learning-based method is devised to systematically synthesise control laws for systems affected by actuator faults, whilst formally certifying the closed-loop stability. The learning architecture trains two Artificial Neural Networks, one representing the control law, and the other resembling a Control Lyapunov Function (CLF). In parallel, a Satisfiability Modulo Theory solver is employed to certify that the obtained CLF formally guarantees the Lyapunov conditions. The method is showcased for two scenarios, one encompassing the stabilisation of an inverted pendulum with redundant actuators, whilst the other covers the control of an Autonomous Underwater Vehicle. The framework is shown capable of synthesising both linear and nonlinear control laws with minimal hyperparameter tuning and within limited computational resources.

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M3 - Conference contribution

AN - SCOPUS:85179288935

T3 - Proceedings of the IEEE Conference on Decision and Control

SP - 5851

EP - 5856

BT - Proceedings of the 62nd IEEE Conference on Decision and Control (CDC 2023)

PB - IEEE

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ER -

Grande D, Fenucci D, Peruffo A, Anderlini E, Phillips AB, Thomas G et al. Systematic Synthesis of Passive Fault-Tolerant Augmented Neural Lyapunov Control Laws for Nonlinear Systems. In Proceedings of the 62nd IEEE Conference on Decision and Control (CDC 2023). IEEE. 2023. p. 5851-5856. (Proceedings of the IEEE Conference on Decision and Control). doi: 10.1109/CDC49753.2023.10383378

Systematic Synthesis of Passive Fault-Tolerant Augmented Neural Lyapunov Control Laws for Nonlinear Systems

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