Neural Network-Based Distributed Cooperative Learning Control for Multiagent Systems via Event-Triggered Communication | IEEE Journals & Magazine | IEEE Xplore

Neural Network-Based Distributed Cooperative Learning Control for Multiagent Systems via Event-Triggered Communication


Abstract:

In this paper, an event-based distributed cooperative learning (DCL) law is proposed for a group of adaptive neural control systems. The plants to be controlled have iden...Show More

Abstract:

In this paper, an event-based distributed cooperative learning (DCL) law is proposed for a group of adaptive neural control systems. The plants to be controlled have identical structures, but reference signals for each plant are different. During control process, each agent intermittently broadcasts its neural network (NN) weight estimation to its neighboring agents under an event-triggered condition that is only based on its own estimated NN weights. If communication topology is connected and undirected, the NN weights of all neural control systems can converge to a small neighborhood of their optimal values. The generalization ability of NNs is guaranteed in the event-triggered context, that is, the approximation domain of each NN is the union of all system trajectories. Furthermore, a strictly positive lower bound on the interevent intervals is also guaranteed to avoid the Zeno behavior. Finally, a numerical example is given to illustrate the effectiveness of the proposed learning law.
Published in: IEEE Transactions on Neural Networks and Learning Systems ( Volume: 31, Issue: 2, February 2020)
Page(s): 407 - 419
Date of Publication: 04 April 2019

ISSN Information:

PubMed ID: 30969933

Funding Agency:


References

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