PI-Type Iterative Learning Consensus Control for Second-Order Hyperbolic Distributed Parameter Models Multi-Agent Systems

This paper considers the consensus control problem of multi-agent systems (MAS) with second-order hyperbolic distributed parameter models. Based on the framework of network topologies, a PI-type iterative learning control protocol is proposed by using the nearest neighbor knowledge. Using Gronwall i...

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Main Authors: Peng Li, Xiao-Qing Liu, Yong-Hong Lan
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8950091/
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author Peng Li
Xiao-Qing Liu
Yong-Hong Lan
author_facet Peng Li
Xiao-Qing Liu
Yong-Hong Lan
author_sort Peng Li
collection DOAJ
description This paper considers the consensus control problem of multi-agent systems (MAS) with second-order hyperbolic distributed parameter models. Based on the framework of network topologies, a PI-type iterative learning control protocol is proposed by using the nearest neighbor knowledge. Using Gronwall inequality, a sufficient condition for the convergence of the consensus errors with respect to the iteration index is obtained. Finally, the validity of the proposed method is verified by two numerical examples.
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spelling doaj.art-e0c710e1aa2545c189dd6d9b8614e1df2022-12-21T18:13:47ZengIEEEIEEE Access2169-35362020-01-018160491605610.1109/ACCESS.2020.29639918950091PI-Type Iterative Learning Consensus Control for Second-Order Hyperbolic Distributed Parameter Models Multi-Agent SystemsPeng Li0https://orcid.org/0000-0001-6400-8851Xiao-Qing Liu1https://orcid.org/0000-0002-9667-4178Yong-Hong Lan2https://orcid.org/0000-0002-7072-4798School of Information Engineering, Xiangtan University, Xiangtan, ChinaDepartment of Precision Instrument, Tsinghua University, Beijing, ChinaSchool of Information Engineering, Xiangtan University, Xiangtan, ChinaThis paper considers the consensus control problem of multi-agent systems (MAS) with second-order hyperbolic distributed parameter models. Based on the framework of network topologies, a PI-type iterative learning control protocol is proposed by using the nearest neighbor knowledge. Using Gronwall inequality, a sufficient condition for the convergence of the consensus errors with respect to the iteration index is obtained. Finally, the validity of the proposed method is verified by two numerical examples.https://ieeexplore.ieee.org/document/8950091/Multi-agent systemsiterative learning controlGronwall inequalityhyperbolic distributed parameter system
spellingShingle Peng Li
Xiao-Qing Liu
Yong-Hong Lan
PI-Type Iterative Learning Consensus Control for Second-Order Hyperbolic Distributed Parameter Models Multi-Agent Systems
IEEE Access
Multi-agent systems
iterative learning control
Gronwall inequality
hyperbolic distributed parameter system
title PI-Type Iterative Learning Consensus Control for Second-Order Hyperbolic Distributed Parameter Models Multi-Agent Systems
title_full PI-Type Iterative Learning Consensus Control for Second-Order Hyperbolic Distributed Parameter Models Multi-Agent Systems
title_fullStr PI-Type Iterative Learning Consensus Control for Second-Order Hyperbolic Distributed Parameter Models Multi-Agent Systems
title_full_unstemmed PI-Type Iterative Learning Consensus Control for Second-Order Hyperbolic Distributed Parameter Models Multi-Agent Systems
title_short PI-Type Iterative Learning Consensus Control for Second-Order Hyperbolic Distributed Parameter Models Multi-Agent Systems
title_sort pi type iterative learning consensus control for second order hyperbolic distributed parameter models multi agent systems
topic Multi-agent systems
iterative learning control
Gronwall inequality
hyperbolic distributed parameter system
url https://ieeexplore.ieee.org/document/8950091/
work_keys_str_mv AT pengli pitypeiterativelearningconsensuscontrolforsecondorderhyperbolicdistributedparametermodelsmultiagentsystems
AT xiaoqingliu pitypeiterativelearningconsensuscontrolforsecondorderhyperbolicdistributedparametermodelsmultiagentsystems
AT yonghonglan pitypeiterativelearningconsensuscontrolforsecondorderhyperbolicdistributedparametermodelsmultiagentsystems