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

Yang YANG; Fanming HUANG; Dong YUE

doi:10.1631/FITEE.2300532

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

Regular Papers | Updated：2025-04-03

- Reinforcement learning based privacy-preserving consensus tracking control of nonstrict-feedback discrete-time multi-agent systems
  Enhanced Publication
- 基于强化学习的非严格反馈离散时间多智能体系统隐私保护一致性跟踪控制
- Frontiers of Information Technology & Electronic Engineering Vol. 26, Issue 3, Pages: 456-471(2025)
- Affiliations：
  
  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
- Author bio：
  
  E-mail: yyang@njupt.edu.cn
  ‡Corresponding author
- Funds：
- DOI：10.1631/FITEE.2300532
  CLC： TP13
- Received：06 August 2023，
  
  Revised：22 November 2023，
  
  Published：2025-03
- Accepted：
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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, 2025, 26(3): 456-471.
DOI：

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, 2025, 26(3): 456-471. DOI： 10.1631/FITEE.2300532.

摘要

本文研究了一类非严格反馈离散时间多智能体系统的隐私保护一致性跟踪问题。为减轻明文加密和解密之间的误差影响，开发一种改进的Liu加密系统，以确保明文信息恢复良好。采用强化学习技术补偿未知动态和真实信号与解密信号之间的误差。采用反步法和图论知识，设计基于强化学习的隐私保护一致性跟踪控制策略。借助李雅普诺夫稳定性理论，证明多智能体系统的一致跟踪误差和所有信号最终有界。最后，通过仿真实例验证设计控制策略的有效性。

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

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

Keywords

references

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