scholarly journals Containment Control for Sampled-data Multi-agent Systems Using the PD-like Protocols

2016 ◽  
Author(s):  
Lina Rong ◽  
Jinxing Lin
Keyword(s):  
Sampled Data ◽  
Multi Agent Systems ◽  
Containment Control ◽  
Agent Systems ◽  
Multi Agent

Formation-containment control of sampled-data second-order multi-agent systems with sampling delay

2018 ◽  
Vol 40 (16) ◽  
pp. 4369-4381 ◽  
Author(s):  
Baojie Zheng ◽  
Xiaowu Mu
Keyword(s):  
Matrix Theory ◽  
Sufficient Conditions ◽  
Second Order ◽  
Control Problems ◽  
Sampled Data ◽  
Multi Agent Systems ◽  
Containment Control ◽  
Agent Systems ◽  
Multi Agent ◽  
Theoretical Results

The formation-containment control problems of sampled-data second-order multi-agent systems with sampling delay are studied. In this paper, we assume that there exist interactions among leaders and that the leader’s neighbours are only leaders. Firstly, two different control protocols with sampling delay are presented for followers and leaders, respectively. Then, by utilizing the algebraic graph theory and matrix theory, several sufficient conditions are obtained to ensure that the leaders achieve a desired formation and that the states of the followers converge to the convex hull formed by the states of the leaders, i.e. the multi-agent systems achieve formation containment. Furthermore, an explicit expression of the formation position function is derived for each leader. An algorithm is provided to design the gain parameters in the protocols. Finally, a numerical example is given to illustrate the effectiveness of the obtained theoretical results.

Neural-network-based containment control of nonlinear multi-agent systems under communication constraints

2016 ◽  
Vol 36 (2) ◽  
pp. 179-185 ◽  
Author(s):  
Chao Ma
Keyword(s):  
Neural Network ◽  
Continuous Time ◽  
Sampled Data ◽  
Multi Agent Systems ◽  
Communication Constraints ◽  
Containment Control ◽  
Content Type ◽  
Agent Systems ◽  
The Neural Network ◽  
Multi Agent

Purpose The purpose of this paper is to investigate the neural-network-based containment control of multi-agent systems with unknown nonlinear dynamics. Moreover, communication constraints are taken into account to reflect more realistic communication networks. Design/methodology/approach Based on the approximation property of the radial basis function neural networks, the control protocol for each agent is designed, where all the information is exchanged in the form of sampled data instead of ideal continuous-time communications. Findings By utilizing the Lyapunov stability theory and the Lyapunov–Krasovskii functional approach, sufficient conditions are developed to guarantee that all the followers can converge to the convex hull spanned by the stationary leaders. Originality/value As ideal continuous-time communications of the multi-agent systems are very difficult or even unavailable to achieve, the neural-network-based containment control of nonlinear multi-agent systems is solved under communication constraints. More precisely, sampled-data information is exchanged, which is more applicable and practical in the real-world applications.

Distributed sampled-data containment control of linear multi-agent systems with fixed topology

2017 ◽  
Vol 11 (14) ◽  
pp. 2299-2306 ◽  
Author(s):  
Wenbing Zhang ◽  
Zidong Wang ◽  
Yurong Liu ◽  
Derui Ding ◽  
Fuad E. Alsaadi
Keyword(s):  
Sampled Data ◽  
Multi Agent Systems ◽  
Containment Control ◽  
Agent Systems ◽  
Multi Agent ◽  
Fixed Topology
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