Simulation de l'évolution du peuplement par les systèmes multi-agents (Simulation of a settlement system evolution by a multi-agent system)

1997 ◽  
Vol 74 (4) ◽  
pp. 385-396
Author(s):  
Lena Sanders ◽  
Denise Pumain ◽  
Hélène Mathian ◽  
F. Guérin-Page ◽  
S. Bura
Keyword(s):  
Multi Agent System ◽  
Settlement System ◽  
Agent System ◽  
System Evolution ◽  
Multi Agent ◽  
Multi Agents

High gain feedback robust control for flocking of multi-agents system

2019 ◽  
Vol 41 (13) ◽  
pp. 3769-3776
Author(s):  
Qing Zhang ◽  
Jie Wang ◽  
Zhengquan Yang ◽  
Zengqiang Chen
Keyword(s):  
Robust Control ◽  
Lyapunov Stability ◽  
Nonlinear Dynamic System ◽  
High Gain ◽  
Multi Agent System ◽  
Agent System ◽  
Velocity Error ◽  
Multi Agent ◽  
Theory Of Lyapunov Stability ◽  
Multi Agents

This paper focuses on the detailed illustration over the feedback robust control of high gain for flocking of the multi-agent system. As for the second-order unknown bounded nonlinear dynamic system, the designed controller has feedback robust control of high gain. Under the action of the leader, the flocking of the multi-agent system established on the basis of high gain feedback robust control can be realized. By employing the theory of Lyapunov stability, it is observed that the velocity error between agents approaches to zero, and no collision occurs between agents. It is further proved by the simulation that the high gain feedback robust control for flocking of multi-agent system can be obtained accordingly. Compared with Qing et al. (2014), high gain feedback robust control for flocking of multi-agent system has better stability.

Deployment of an Arbitrary Distribution of a Multi-Agent System With Finite Size on a Desired Formation

2016 ◽  
Author(s):  
Hossein Rastgoftar ◽  
Ella M. Atkins
Keyword(s):  
Collision Avoidance ◽  
Finite Size ◽  
Arbitrary Distribution ◽  
Multi Agent System ◽  
Agent System ◽  
Local Communication ◽  
One Dimensional ◽  
System Evolution ◽  
Multi Agent ◽  
Motion Problems

This paper considers the problem of deploying an arbitrary multi-agent system in a desired formation over an n-dimensional motion space. Each agent is considered to be a ball and collision avoidance is addressed. System evolution in ℝn is decomposed into n one dimensional motion problems, where evolution of the agents qth (q = 1, 2, 3) components are independently guided by two q-leaders. The remaining agents are considered q-followers, updating the qth component of their positions by local interactions with two neighboring q-agents. Communications among the q-agents are weighted by values consistent with the qth position components of agents in the desired configuration. This paper shows how specifying certain constraints on q-leader motion can address the problem of inter-agent collision avoidance when followers acquire their desired positions only by local communication.

Modeling of Multi-Agents' Coordination

2013 ◽  
Vol 437 ◽  
pp. 222-225
Author(s):  
Mei Zhang ◽  
Jing Hua Wen ◽  
Yong Long Fan
Keyword(s):  
Genetic Algorithm ◽  
Multi Agent System ◽  
Agent System ◽  
Agent Based ◽  
Virtual Geographic Environment ◽  
Multi Agent ◽  
Geographic Environment ◽  
Behaviour Learning ◽  
Centralized System ◽  
Multi Agents

It takes cooperation among multi-user in virtual geographic environment (VGE) based on Multi-Agent System (MAS) in the centralized system as researched object. Then we detailed analyze and research arithmetic of collectivistic operating behaviour learning of Multi-Agent based on Genetic Algorithm (GA). Finally we design an example which shows how 3 evolutional Agents cooperate to complete the task of colony pushing cylinder box.

Sign in / Sign up

Export Citation Format

Share Document