scholarly journals Model-Free Optimal Control of Linear Multi-Agent Systems via Decomposition and Hierarchical Approximation

2021 ◽  
pp. 1-1
Author(s):  
Gangshan Jing ◽  
He Bai ◽  
Jemin George ◽  
Aranya Chakrabortty
Keyword(s):  
Optimal Control ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Model Free ◽  
Multi Agent ◽  
Hierarchical Approximation

Data-driven tracking consensus for a class of unknown nonlinear multi-agent systems

2021 ◽  
pp. 107754632110340
Author(s):  
Jia Wu ◽  
Ning Liu ◽  
Wenyan Tang
Keyword(s):  
Control Method ◽  
Sufficient Conditions ◽  
Data Driven ◽  
The Novel ◽  
Multi Agent Systems ◽  
Tracking Errors ◽  
Agent Systems ◽  
Model Free ◽  
Strongly Connected ◽  
Multi Agent

This study investigates the tracking consensus problem for a class of unknown nonlinear multi-agent systems A novel data-driven protocol for this problem is proposed by using the model-free adaptive control method To obtain faster convergence speed, one-step-ahead desired signal is introduced to construct the novel protocol Here, switching communication topology is considered, which is not required to be strongly connected all the time Through rigorous analysis, sufficient conditions are given to guarantee that the tracking errors of all agents are convergent under the novel protocol Examples are given to validate the effectiveness of results derived in this article

Sign in / Sign up

Export Citation Format

Share Document