2022/10/18 by William D’Amico, Alessio La Bella, D'Amico, William +3
Computer Science · Physics and Astronomy · #FOS: Electrical engineering #Model Reduction and Neural Networks #Neural Networks Stability and Synchronization #Neural Networks and Applications #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2210.09721
openalex publication_date 2022/10/18 · openalex created_date 2023/02/13 · openalex updated_date 2026/07/28
This paper proposes a novel sufficient condition for the incremental input-to-state stability of a generic class of recurrent neural networks (RNNs). The established condition is compared with others available in the literature, showing to be less conservative. Moreover, it can be applied for the design of incremental input-to-state stable RNN-based control systems, resulting in a linear matrix inequality constraint for some specific RNN architectures. The formulation of nonlinear observers for the considered system class, as well as the design of control schemes with explicit integral action, are also investigated. The theoretical results are validated through simulation on a referenced nonlinear system.