ConsensusofMulti-AgentSystemswithBinary-Val

本书主要介绍在集值量测情况下的多智能体同步控制。首先，针对集值量测系统，介绍了两种辨识算法——无截断辨识算法和递推投影辨识算法，并分析了算法的性质；其次，基于两种辨识算法研究了不同拓扑结构不同系统动态下的多智能体同步控制，包括无向拓扑结构下的集值多智能体系统的同步控制、有向拓扑结构下的集值多智能体系统的同步控制、集值线性多智能体的同步控制，针对不同问题设计了不同的控制算法，并分别给出了同步控制速度。本书可供从事控制科学与工程、运筹学与控制理论、系统理论等专业人员阅读，也可供高等院校相关专业的师生参考。

1 Introduction
1.1 Motivation
1.2 Identification of Set-Valued Systems
1.3 Consensus Control of Multi-Agent Systems
1.4 Outline of the Book
2 Preliminaries
2.1 Vectors and Norms
2.2 Probability Theory
2.3 Algebraic Graph Theory
2.4 Some Other Concepts
2.5 Notes
3 Identification of Set-Valued Systems
3.1 Problem Formulation
3.2 Asymptotically Efficient Non-Truncated Identification Algorithm
3.2.1 Identification for Single-Parameter Systems
3.2.2 Identification for Multi-Parameter Systems
3.2.3 Numerical Simulation
3.3 Recursive Projection Identification Algorithm
3.3.1 Algorithm Design
3.3.2 Properties of the Algorithm
3.3.3 Numerical Simulation
3.4 Notes
4 Consensus with Binary-Valued Measurements under Undirected Topology
4.1 Problem Formulation
4.2 Two-Time-Scale Consensus
4.2.1 Estimation
4.2.2 Consensus Control
4.2.3 The Consensus Protocol
4.2.4 Numerical Simulation
4.3 Recursive Projection Consensus
4.3.1 Consensus Algorithm
4.3.2 Main Results
4.3.3 Numerical Simulation
4.4 Notes
5 Consensus with Binary-Valued Measurements under Directed Topology
5.1 Problem Formulation
5.2 Control Algorithm
5.3 Properties of the Algorithm
5.3.1 Estimation
5.3.2 Transportation Design
5.4 Numerical Simulation
5.5 Notes
6 Consensus of Linear Multi-Agent Systems with Binary-Valued Measurements
6.1 Problem Formulation
6.2 Review on the Case of Precise Communication
6.3 Case of Binary-Valued Communication
6.3.1 Control Algorithm
6.3.2 Main Results
6.4 Numerical Simulation
6.5 Notes
References