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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Neural Network-Based Distributed Cooperative Learning Control for Multiagent Systems via Event-Triggered Communication
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Neural Network-Based Distributed Cooperative Learning Control for Multiagent Systems via Event-Triggered Communication

机译：基于事件触发通信的基于神经网络的多智能体分布式协作学习控制

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摘要

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.
机译：本文针对一组自适应神经控制系统，提出了一种基于事件的分布式合作学习法。要控制的植物具有相同的结构，但是每个植物的参考信号都不同。在控制过程中，每个代理在仅基于其自身估计的NN权重的事件触发条件下，将其神经网络（NN）权重估计间歇地广播到其相邻的代理。如果通信拓扑是连接的且是无向的，则所有神经控制系统的NN权重都可以收敛到其最佳值的一小部分。在事件触发的上下文中，可以保证神经网络的泛化能力，也就是说，每个神经网络的逼近域是所有系统轨迹的并集。此外，还确保了事件间隔上严格的正下限，以避免Zeno行为。最后，通过数值例子说明了所提出的学习规律的有效性。

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