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

Conference paper (2023)

Authors

Davide Grande National Oceanography Center, University College London
Davide Fenucci National Oceanography Center
A. Peruffo Team Manuel Mazo Jr - Mechanical, Maritime and Materials Engineering
Enrico Anderlini University College London
Alexander B. Phillips National Oceanography Center
G Thomas University College London
Georgios Salavasidis National Oceanography Center
Research Group
Team Manuel Mazo Jr (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2023 Davide Grande, Davide Fenucci, A. Peruffo, Enrico Anderlini, Alexander B. Phillips, Giles Thomas, Georgios Salavasidis
DOI
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https://doi.org/10.1109/CDC49753.2023.10383378
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Published Date

2023

Language

English

Reuse Rights

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Manuel Mazo Jr

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.