Reinforcement learning based privacy-preserving consensus tracking control of nonstrict-feedback discrete-time multi-agent systems  

作　　者：Yang YANG Fanming HUANG Dong YUE 

机构地区：[1]College of Automation&College of Artificial Intelligence,Nanjing University of Posts and Telecommunications,Nanjing 210023,China [2]Advanced Technology Institute for Carbon Neutrality,Nanjing University of Posts and Telecommunications,Nanjing 210023,China

出　　处：《Frontiers of Information Technology & Electronic Engineering》2025年第3期456-471,共16页信息与电子工程前沿(英文版)

基　　金：Project supported by the National Natural Science Foundation of China(Nos.62473204 and 61873130);the"Chunhui Program"Collaborative Scientific Research Project,China(No.202202004);the Natural Science Foundation of Nanjing University of Posts and Telecommunications,China(Nos.NY221082,NY222144,and NY223075);the Huali Program for Excellent Talents in Nanjing University of Posts and Telecommunications,China。

摘　　要：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,an 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.

关 键 词：Multi-agent systems Consensus tracking PRIVACY-PRESERVING Reinforcement learning 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程]