论文标题
在事件触发的通信下，具有深度神经网络的分布式状态估计，用于不确定的非线性系统
Distributed State Estimation with Deep Neural Networks for Uncertain Nonlinear Systems under Event-Triggered Communication
论文作者
论文摘要
通过使用分布式和事件触发的观察者，检查了针对重建系统状态的传感器网络的分布式状态估计。传感器网络中的每个代理都采用深层神经网络（DNN）来近似系统的不确定的非线性动力学，该动力学是使用多个时间尺度方法训练的。具体而言，使用基于Lyapunov的梯度下降更新定律在线更新每个DNN的外权重，而内部权重和偏见是使用监督学习方法离线训练的，并收集了输入输出数据。观察者利用事件触发的通信来促进网络资源的有效利用。非滑lyapunov分析表明，分布式事件触发的观察者具有统一的最终有限状态重建误差。提供了一项模拟研究来验证结果并证明DNN提供的性能提高。
Distributed state estimation is examined for a sensor network tasked with reconstructing a system's state through the use of a distributed and event-triggered observer. Each agent in the sensor network employs a deep neural network (DNN) to approximate the uncertain nonlinear dynamics of the system, which is trained using a multiple timescale approach. Specifically, the outer weights of each DNN are updated online using a Lyapunov-based gradient descent update law, while the inner weights and biases are trained offline using a supervised learning method and collected input-output data. The observer utilizes event-triggered communication to promote the efficient use of network resources. A nonsmooth Lyapunov analysis shows the distributed event-triggered observer has a uniformly ultimately bounded state reconstruction error. A simulation study is provided to validate the result and demonstrate the performance improvements afforded by the DNNs.
