Fault Tolerant Control for Autonomous Surface Vehicles via Model Reference Reinforcement Learning

Qingrui Zhang, Xinyu Zhang, Bo Zhu, V. Reppa

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

A novel fault tolerant control algorithm is proposed in this paper based on model reference reinforcement learning for autonomous surface vehicles subject to sensor faults and model uncertainties. The proposed control scheme is a combination of a model-based control approach and a data-driven method, so it can leverage the advantages of both sides. The proposed design contains a baseline controller that ensures stable tracking performance at healthy conditions, a fault observer that estimates sensor faults, and a reinforcement learning module that learns to accommodate sensor faults using fault estimation and compensate for model uncertainties. The impact of sensor faults and model uncertainties can be effectively mitigated by this composite design. Stable tracking performance can also be ensured even at both the offline training and online implementation stages for the learning-based fault tolerant control. A numerical simulation with gyro sensor faults is presented to demonstrate the efficiency of the proposed algorithm.
Original languageEnglish
Title of host publicationProceedings of the 60th IEEE Conference on Decision and Control (CDC 2021)
PublisherIEEE
Pages1536-1541
ISBN (Print)978-1-6654-3659-5
DOIs
Publication statusPublished - 2021
Event60th IEEE Conference on Decision and Control (CDC 2021) - Austin, United States
Duration: 14 Dec 202117 Dec 2021

Conference

Conference60th IEEE Conference on Decision and Control (CDC 2021)
Country/TerritoryUnited States
CityAustin
Period14/12/2117/12/21

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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