Data-driven control of echo state-based recurrent neural networks with robust stability guarantees

被引：0
|
作者：
D'Amico, William [1 ]
La Bella, Alessio [1 ]
Farina, Marcello [1 ]
机构：
[1] Politecn Milan, Dipartimento Elettron, Informaz & Bioingn, Via Ponzio 34-5, I-20133 Milan, Italy
来源：
SYSTEMS & CONTROL LETTERS | 2025年 / 195卷
关键词：
Recurrent neural networks; Linear matrix inequalities; Data-based control; CONTROL DESIGN; SYSTEMS;
D O I：
10.1016/j.sysconle.2024.105974
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this work we propose a new data-based approach for robust controller design for a rather general class of recurrent neural networks affected by bounded measurement noise. We first identify the model set compatible with available data in a selected model class via set membership (SM). Then, incremental input-to-state stability and desired performances for the closed loop system are enforced robustly to all models in the identified model set via a linear matrix inequality (LMI) optimization problem. Numerical results show the effectiveness of the comprehensive method.
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页数：9
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