Deep Learning-Based Consensus Control of a Multi-Agents System with Unknown Time-Varying Delay

Despite the enormous progress in consensus control of a multi-agents system (MAS), amodel-based consensus control is valid only when the assumption on the system environment and on the model is valid. To overcome this limitation, several deep learning (DL) based consensus controls directly learn how...

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Main Author: Janghoon Yang
Format: Article
Language:English
Published: MDPI AG 2022-04-01
Series:Electronics
Subjects:
Online Access:https://www.mdpi.com/2079-9292/11/8/1176
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author Janghoon Yang
author_facet Janghoon Yang
author_sort Janghoon Yang
collection DOAJ
description Despite the enormous progress in consensus control of a multi-agents system (MAS), amodel-based consensus control is valid only when the assumption on the system environment and on the model is valid. To overcome this limitation, several deep learning (DL) based consensus controls directly learn how to generate a control signal from the model-based control. Depending on the exploitation of knowledge from the model-based control structure, four different deep learning models were considered. Numerical simulations of MAS with unknown time-varying delays and disturbance verify that, while providing comparable performance to the model-based control for many different system configurations, the DL-based controls with explicit knowledge of the control signal structure are preferred to that with implicit knowledge of the control signal or no knowledge, which shows the promising potential of DL-based control with supervised learning.
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spelling doaj.art-b85c22c90d70426da3d371323ac1ef832023-11-30T21:01:24ZengMDPI AGElectronics2079-92922022-04-01118117610.3390/electronics11081176Deep Learning-Based Consensus Control of a Multi-Agents System with Unknown Time-Varying DelayJanghoon Yang0AI Software Engineering, Seoul Media Institute of Technology, Seoul 07590, KoreaDespite the enormous progress in consensus control of a multi-agents system (MAS), amodel-based consensus control is valid only when the assumption on the system environment and on the model is valid. To overcome this limitation, several deep learning (DL) based consensus controls directly learn how to generate a control signal from the model-based control. Depending on the exploitation of knowledge from the model-based control structure, four different deep learning models were considered. Numerical simulations of MAS with unknown time-varying delays and disturbance verify that, while providing comparable performance to the model-based control for many different system configurations, the DL-based controls with explicit knowledge of the control signal structure are preferred to that with implicit knowledge of the control signal or no knowledge, which shows the promising potential of DL-based control with supervised learning.https://www.mdpi.com/2079-9292/11/8/1176consensusmulti-agents systemdeep learningdelay
spellingShingle Janghoon Yang
Deep Learning-Based Consensus Control of a Multi-Agents System with Unknown Time-Varying Delay
Electronics
consensus
multi-agents system
deep learning
delay
title Deep Learning-Based Consensus Control of a Multi-Agents System with Unknown Time-Varying Delay
title_full Deep Learning-Based Consensus Control of a Multi-Agents System with Unknown Time-Varying Delay
title_fullStr Deep Learning-Based Consensus Control of a Multi-Agents System with Unknown Time-Varying Delay
title_full_unstemmed Deep Learning-Based Consensus Control of a Multi-Agents System with Unknown Time-Varying Delay
title_short Deep Learning-Based Consensus Control of a Multi-Agents System with Unknown Time-Varying Delay
title_sort deep learning based consensus control of a multi agents system with unknown time varying delay
topic consensus
multi-agents system
deep learning
delay
url https://www.mdpi.com/2079-9292/11/8/1176
work_keys_str_mv AT janghoonyang deeplearningbasedconsensuscontrolofamultiagentssystemwithunknowntimevaryingdelay