用递归神经网络设计鲁棒控制器，提升系统对干扰的适应性。
Recurrent neural network-based robust control systems with regional properties and application to MPC design
- 基于线性矩阵不等式设计观测器与状态反馈控制器
- 实现恒定设定点跟踪，且在扰动下保持稳定
- 适合需要高鲁棒性的工业过程控制场景
本文研究一类递归神经网络描述系统的输出反馈控制方案设计。提出一种基于线性矩阵不等式的算法，用于设计观测器和静态状态反馈控制器。该方法利用全局与区域增量输入-状态稳定性（incremental ISS），实现恒定设定点跟踪，确保对扰动和状态估计不确定性的鲁棒性。为克服区域增量ISS的局限性，引入替代方案：将静态控制律替换为基于管状的非线性模型预测控制器（NMPC），利用区域增量ISS特性。证明该条件可保证鲁棒NMPC律的收敛性与递归可行性，扩大吸引域。理论结果通过pH中和过程基准测试的数值仿真验证。
原文摘要 · Abstract (English)
This paper investigates the design of output-feedback schemes for systems described by a class of recurrent neural networks. We propose a procedure based on linear matrix inequalities for designing an observer and a static state-feedback controller. The algorithm leverages global and regional incremental input-to-state stability (incremental ISS) and enables the tracking of constant setpoints, ensuring robustness to disturbances and state estimation uncertainty. To address the potential limitations of regional incremental ISS, we introduce an alternative scheme in which the static law is replaced with a tube-based nonlinear model predictive controller (NMPC) that exploits regional incremental ISS properties. We show that these conditions enable the formulation of a robust NMPC law with guarantees of convergence and recursive feasibility, leading to an enlarged region of attraction. Theoretical results are validated through numerical simulations on the pH-neutralisation process benchmark.
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