Convergence speed in formation control of multi-agent systems - A robust control approach

2013 ◽  
Author(s):  
Ulf Pilz ◽  
Herbert Werner
Keyword(s):  
Robust Control ◽  
Formation Control ◽  
Convergence Speed ◽  
Multi Agent Systems ◽  
Control Approach ◽  
Agent Systems ◽  
Multi Agent

scholarly journals Robust adaptive formation control of quadcopters based on a leader–follower approach

2019 ◽  
Vol 16 (4) ◽  
pp. 172988141986273 ◽  
Author(s):  
Nguyen Xuan-Mung ◽  
Sung Kyung Hong
Keyword(s):  
Formation Control ◽  
Control Algorithm ◽  
Multi Agent Systems ◽  
Control Approach ◽  
Agent Systems ◽  
Model Reference Control ◽  
Formation Pattern ◽  
Multi Agent ◽  
Heading Angle ◽  
Robust Adaptive

The formation control problem for multi-agent systems has been explored in recent years. However, controlling a formation of multiple aerial vehicles in the presence of disturbances has been a challenge for control researchers. To deal with this issue, a robust adaptive formation control algorithm for a group of multiple quadcopters is proposed. A nonlinear model of the dynamics of the formation error is obtained based on a leader–follower scheme. This model considers both the relative position in the x– y plane and the relative heading angle between vehicles in the presence of uncertainties. In addition, by means of a model reference control approach, a robust adaptive formation controller is used to steer the vehicles into a formation pattern and have them maintain the formation shape. Numerical simulations demonstrate the effectiveness of the algorithm.

Fuzzy Sliding Mode Control of Multi-Agent Systems Using Artificial Potentials

2010 ◽  
Author(s):  
F. Roshanghalb ◽  
J. Mortazavi ◽  
A. Alasty ◽  
H. Sayyaadi
Keyword(s):  
Formation Control ◽  
Fuzzy Controller ◽  
Sliding Mode ◽  
Dimensional Space ◽  
Control Parameters ◽  
Multi Agent Systems ◽  
Control Approach ◽  
Agent Systems ◽  
Multi Agent ◽  
Fuzzy Control Strategy

In this paper a fuzzy control strategy of autonomous multi-agent systems is presented. The main purpose is to obtain an improvement on the results of designed sliding mode controllers in previous articles using supervisory fuzzy controller. Similarly, a quasi-static swarm model in n-dimensional space is introduced wherein the inter-individual interactions are based on artificial potential functions; and the motions of members are in direction with the negative gradient of the combined potentials which are the result of a balance between inter-individual interactions and the simultaneous interactions of the swarm members with their environment. Then a general model for vehicle dynamics of each agent has been studied. The aim is to reach the desirable formation of swarm by forcing agent’s motion to follow the quasi-static model. In literature a sliding mode controller is used to perform this task. In this work, same control structure is applied with a difference in control parameters. In this paper, fuzzy rules are introduced for some major control parameters which are efficacious on the results in order to get better performances along with less error during the formation control approach.

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