Augmented Neural Lyapunov Control

Grande, Davide; Peruffo, Andrea; Anderlini, Enrico; Salavasidis, Georgios. 2023 Augmented Neural Lyapunov Control. IEEE Access, 11. 67979-67986. 10.1109/ACCESS.2023.3291349

Before downloading, please read NORA policies.

[thumbnail of Augmented_Neural_Lyapunov_Control.pdf]

Preview

Text
Augmented_Neural_Lyapunov_Control.pdf - Published Version
Available under License Creative Commons Attribution 4.0.
Download (2MB) | Preview

Official URL: http://dx.doi.org/10.1109/ACCESS.2023.3291349

Abstract/Summary

Machine learning-based methodologies have recently been adapted to solve control problems. The Neural Lyapunov Control (NLC) method is one such example. This approach combines Artificial Neural Networks (ANNs) with Satisfiability Modulo Theories (SMT) solvers to synthesise stabilising control laws and to prove their formal correctness. The ANNs are trained over a dataset of state-space samples to generate candidate control and Lyapunov functions, while the SMT solvers are tasked with certifying the correctness of the Lyapunov function over a continuous domain or by returning a counterexample. Despite the approach’s attractiveness, issues can occur due to subsequent calls of the SMT module at times returning similar counterexamples, which can turn out to be uninformative and may lead to dataset overfitting. Additionally, the control network weights are usually initialised with pre-computed gains from state-feedback controllers, e.g. Linear-Quadratic Regulators. To properly perform the initialisation requires user time and control expertise. In this work, we present an Augmented NLC method that mitigates these drawbacks, removes the need for the control initialisation and further improves counterexample generation. As a result, the proposed method allows the synthesis of nonlinear (as well as linear) control laws with the sole requirement being the knowledge of the system dynamics. The ANLC is tested over challenging benchmarks such as the Lorenz attractor and outperformed existing methods in terms of successful synthesis rate. The developed framework is released open-source at: https://github.com/grande-dev/Augmented-Neural-Lyapunov-Control .

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1109/ACCESS.2023.3291349
ISSN:	2169-3536
Additional Keywords:	Computer-aided control design, Lyapunov methods, neural networks
Date made live:	25 Jul 2023 12:04 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/535467

Item Type:

Publication - Article

Digital Object Identifier (DOI):

10.1109/ACCESS.2023.3291349

ISSN:

2169-3536

Additional Keywords:

Computer-aided control design, Lyapunov methods, neural networks

Date made live:

25 Jul 2023 12:04 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/535467