Coordination of low-power nonlinear multi-agent systems using cloud computing and a data-driven hybrid predictive control method

2021 ◽  
Vol 108 ◽  
pp. 104722
Author(s):  
Haoran Tan ◽  
Yaonan Wang ◽  
Hang Zhong ◽  
Min Wu ◽  
Yiming Jiang
Keyword(s):  
Cloud Computing ◽  
Low Power ◽  
Predictive Control ◽  
Control Method ◽  
Data Driven ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent

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

scholarly journals Adaptive constraint control for nonlinear multi-agent systems with undirected graphs

2021 ◽  
Vol 6 (11) ◽  
pp. 12051-12064
Author(s):  
Lu Zhi ◽  
◽  
Jinxia Wu
Keyword(s):  
Control Method ◽  
Control Technique ◽  
Multi Agent Systems ◽  
Distributed Consensus ◽  
Controlled System ◽  
Agent Systems ◽  
Full State ◽  
Constraint Control ◽  
Multi Agent ◽  
System States

<abstract><p>This paper investigates the problem of adaptive distributed consensus control for stochastic multi-agent systems (MASs) with full state constraints. By utilizing adaptive backstepping control technique and barrier Lyapunov function (BLF), an adaptive distributed consensus constraint control method is proposed. The developed control method can ensure that all signals of the controlled system are semi-globally uniformly ultimately bounded (SGUUB) in probability, and outputs of the follower agents keep consensus with the output of leader. In addition, system states are not transgressed their constrained sets. Finally, simulation results are provided to illustrate the feasibility of the developed control algorithm and theorem.</p></abstract>

Sign in / Sign up

Export Citation Format

Share Document