Regional stability conditions for recurrent neural network-based control systems
<|reference_start|>Regional stability conditions for recurrent neural network-based control systems: 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.<|reference_end|>
@article{la bella2024regional,
    title={Regional stability conditions for recurrent neural network-based control
  systems},
    author={Alessio La Bella and Marcello Farina and William D'Amico and Luca
  Zaccarian},
    journal={arXiv preprint arXiv:2409.15792},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.15792},
    primaryClass={eess.SY cs.SY}
}