In this paper we propose novel global and regional stability analysis conditions based on linear matrix inequalities for a general class of recurrent neural networks. These conditions can be also used for state-feedback control design and a suitable optimization problem enforcing H2 norm minimization properties is defined. The theoretical results are corroborated by numerical simulations, showing the advantages and limitations of the methods presented herein.
Regional stability conditions for recurrent neural network-based control systems / La Bella, A., Farina, M., D'Amico, W., Zaccarian, L.. - In: AUTOMATICA. - ISSN 0005-1098. - 174:(2025), pp. 112127-112127. [10.1016/j.automatica.2025.112127]
Regional stability conditions for recurrent neural network-based control systems
Farina, Marcello;Zaccarian, Luca
2025-01-01
Abstract
In this paper we propose novel global and regional stability analysis conditions based on linear matrix inequalities for a general class of recurrent neural networks. These conditions can be also used for state-feedback control design and a suitable optimization problem enforcing H2 norm minimization properties is defined. The theoretical results are corroborated by numerical simulations, showing the advantages and limitations of the methods presented herein.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione
