Affiliation(s):
College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, China;
moreAffiliation(s): College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; Institute of Advanced Technology for Carbon Neutrality, Nanjing University of Posts and Telecommunications, Nanjing 210023, China;
less
Yang YANG, Fanming HUANG, Dong YUE. Reinforcement learning-based privacy-preserving consensus tracking control of nonstrict-feedback discrete-time multi-agent systems[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2300532
@article{title="Reinforcement learning-based privacy-preserving consensus tracking control of nonstrict-feedback discrete-time multi-agent systems", author="Yang YANG, Fanming HUANG, Dong YUE", journal="Frontiers of Information Technology & Electronic Engineering", year="in press", publisher="Zhejiang University Press & Springer", doi="https://doi.org/10.1631/FITEE.2300532" }
%0 Journal Article %T Reinforcement learning-based privacy-preserving consensus tracking control of nonstrict-feedback discrete-time multi-agent systems %A Yang YANG %A Fanming HUANG %A Dong YUE %J Frontiers of Information Technology & Electronic Engineering %P %@ 2095-9184 %D in press %I Zhejiang University Press & Springer doi="https://doi.org/10.1631/FITEE.2300532"
TY - JOUR T1 - Reinforcement learning-based privacy-preserving consensus tracking control of nonstrict-feedback discrete-time multi-agent systems A1 - Yang YANG A1 - Fanming HUANG A1 - Dong YUE J0 - Frontiers of Information Technology & Electronic Engineering SP - EP - %@ 2095-9184 Y1 - in press PB - Zhejiang University Press & Springer ER - doi="https://doi.org/10.1631/FITEE.2300532"
Abstract: This paper investigates a privacy-preserving consensus tracking problem for a class of nonstrict-feedback discrete-time multi-agent systems (MASs). An improved Liu cryptosystem is developed to alleviate the errors between encryption and decryption on the plaintext, which ensures satisfactory recovery of the plaintext information. A reinforcement learning (RL) technique is then employed to compensate for unknown dynamics and errors between true signals and decrypted ones. Based on the backstepping and graph theory, a RL-based privacy-preserving consensus tracking control strategy is further designed. By virtue of graph theory and Lyapunov stability theory, it is shown that the consensus tracking errors and all signals in the MAS are ultimately bounded. Finally, simulation examples are presented for verification of the effectiveness of the control strategy.
Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
Reference
Open peer comments: Debate/Discuss/Question/Opinion
Open peer comments: Debate/Discuss/Question/Opinion
<1>