On the stability properties of Gated Recurrent Units neural networks

Recurrent Neural Network-based Internal Model Control design for stable nonlinear systems

Owing to their superior modeling capabilities, gated Recurrent Neural Networks, such as Gated Recurrent Units (GRUs) and Long Short-Term Memory networks (LSTMs), have become popular tools for learning dynamical systems. This paper aims to discuss how these networks can be adopted for the synthesis of Internal Model Control (IMC) architectures. To this end, first a gated recurrent network is used to learn a model of the unknown input-output stable plant. Then, a controller gated recurrent network is trained to approximate the model inverse. The stability of these networks, ensured by means of a…

基于门控循环单元模型的鲁棒约束非线性模型预测控制——扩展版

Robust constrained nonlinear Model Predictive Control with Gated Recurrent Unit model -- Extended version

本研究提出一种基于门控循环单元网络模型的鲁棒约束非线性模型预测控制方法，通过约束收紧策略保障约束鲁棒满足与递归可行性，引入新终端代价和终端集实现闭环系统的输入-状态稳定性。

闭环控制系统中基于门控循环神经网络的虚拟传感器的Luré-Postnikov稳定性分析

Luré-Postnikov Stability Analysis of Closed-Loop Control Systems with Gated Recurrent Neural Network-based Virtual Sensors

本文提出LP-GRNN架构以解决基于GRU/LSTM的虚拟传感器在闭环控制系统中的稳定性问题，通过统一非线性算子形式将稳定性分析转化为可处理的线性矩阵不等式。

用于基于逆的前馈控制的物理引导门控循环单元

Physics-guided gated recurrent units for inversion-based feedforward control

本文提出将GRU集成到物理引导神经网络框架中形成PG-GRU，通过稳定逆线性模型与残差GRU两步法设计前馈控制器，并在两质量弹簧阻尼系统实验中实现约两倍的积分绝对误差改善。

利用门控循环单元实现大地测量任务迭代式在线精确姿态控制

Leveraging Gated Recurrent Units for Iterative Online Precise Attitude Control for Geodetic Missions

本文提出利用门控循环单元（GRU）学习并预测外部扰动趋势，通过加性校正项迭代改进PID控制器，以提升GRACE-FO等大地测量任务的姿态控制精度，仿真验证了姿态误差显著减小。

基于全参数域麻雀搜索算法和双模块双向门控循环单元的锂离子电池行驶工况健康状态预测

State of health prediction of lithium-ion batteries for driving conditions based on full parameter domain sparrow search algorithm and dual-module bidirectional gated recurrent unit

针对电动汽车锂离子电池健康状态预测，提出融合全参数域麻雀搜索算法与双模块双向门控循环单元的模型，基于牛津数据集和真实行驶数据实现高精度、强鲁棒性的SOH预测。