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Consensus-Based Distributed Cooperative Learning From Closed-Loop Neural Control Systems

机译：闭环神经控制系统基于共识的分布式合作学习

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摘要

In this paper, the neural tracking problem is addressed for a group of uncertain nonlinear systems where the system structures are identical but the reference signals are different. This paper focuses on studying the learning capability of neural networks (NNs) during the control process. First, we propose a novel control scheme called distributed cooperative learning (DCL) control scheme, by establishing the communication topology among adaptive laws of NN weights to share their learned knowledge online. It is further proved that if the communication topology is undirected and connected, all estimated weights of NNs can converge to small neighborhoods around their optimal values over a domain consisting of the union of all state orbits. Second, as a corollary it is shown that the conclusion on the deterministic learning still holds in the decentralized adaptive neural control scheme where, however, the estimated weights of NNs just converge to small neighborhoods of the optimal values along their own state orbits. Thus, the learned controllers obtained by DCL scheme have the better generalization capability than ones obtained by decentralized learning method. A simulation example is provided to verify the effectiveness and advantages of the control schemes proposed in this paper.
机译：在本文中，针对一组系统结构相同但参考信号不同的不确定非线性系统，解决了神经跟踪问题。本文重点研究控制过程中神经网络（NN）的学习能力。首先，通过在神经网络权重的自适应定律之间建立通信拓扑以在线共享其学习的知识，我们提出了一种称为分布式合作学习（DCL）控制方案的新颖控制方案。进一步证明，如果通信拓扑是无向的和连接的，则NN的所有估计权重都可以在由所有状态轨道的并集组成的域上收敛到其最佳值附近的小邻域。其次，作为推论，证明了确定性学习的结论仍然存在于分散式自适应神经控制方案中，但是，NN的估计权重仅收敛于沿其自身状态轨道的最优值的小邻域。因此，与通过分散学习方法获得的控制器相比，通过DCL方案获得的学习控制器具有更好的泛化能力。通过仿真实例验证了本文提出的控制方案的有效性和优势。

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