摘要近年来，随着分布式多智能体系统的迅速发展，多智能体一致性问题已经成为控制领域的热点问题。本文主要介绍了一致性问题在多传感器协同估计中的应用，首先阐述了相关的背景理论知识，包括多传感器信息融合技术、多智能体系统一致性理论和卡尔曼滤波算法；介绍了两种卡尔曼一致滤波算法：KCF算法和简化KCF算，并且给出了丢包情况下的滤波算法；接着通过分析卡尔曼滤波算法的原理对简化KCF算法中的不当之处进行了修正，并且利用仿真验证了修改之后的算法估计性能相比原来的算法性能有所改进；应用数值仿真对比分析了两种算法的估计性能，并进一步研究了迭代步长和丢包率对估计误差的影响，还针对这两种因素产生的影响分别提出了改善方案；最后将牵制控制引入到一致性滤波器中，讨论了特定牵制和随机牵制两种控制策略，并通过仿真对比分析了两种策略的控制效果。64506

毕业论文关键词 多智能体一致性，传感器网络，卡尔曼一致滤波，牵制控制

毕业设计说明书（论文）外文摘要

Title   The distributed consensus of multi-agent systems and its application——Multi-senor Collaborative Estimation      Abstract

In recent years, with the rapid development of distributed multi-agent systems, multi-agent consensus research becomes a hot spot in the field of control science research. This paper mainly introduces the consensus problem in the application of the multi-sensor collaborative estimation.

The first part of this article expounds the background of related theory knowledge, including the multi-sensor information fusion technology, the consensus of the multi-agent system theory and kalman filtering algorithm; Next,two kalman-consensus filtering algorithms are introduced: KCF algorithm and simplified KCF algorithm, then we correct the impropriety of the simplified KCF algorithm, and give a algorithm with packet-dropping. The proposed algorithm is verified by numerical simulations. we compare the estimation performance of the two algorithms, and further study the influence of the iteration step-size and packet-dropping loss on the estimation error, then propose two schemes of improvement considering impact of the two factors. Finally Pinning control is introduced into the consensus filter. Two contain strategies are put forward: specific contain and random contain, then compare their control accuracy through simulation.

Keywords  multi-agent consensus，distributed data fusion，Kalman-Consensus  filtering, Pinning control

目次

第1章 绪论 1

1.1 课题背景及意义 1

1.2 国内外研究现状 1

1.3 多传感器信息融合概述 3

1.3.1 信息融合的概念和特点 3

1.3.2 信息融合算法 4

1.3.3 分布式融合估计机制 5

1.4 本文的内容安排 6

第2章 基于一致性的滤波算法 7

2.1 一致性理论综述 7

2.1.1 一致性问题数学模型 8

2.1.2 一致性算法 8

2.1.3 一致性收敛速度 9

2.1.4 一致均衡状态 9

2.2 卡尔曼滤波算法介绍 10

2.3 卡尔曼一致滤波算法介绍 12

2.3.1 卡尔曼一致滤波算法