[2506.20334] Recurrent neural network-based robust control systems with regional properties and application to MPC design

Electrical Engineering and Systems Science > Systems and Control arXiv:2506.20334 (eess) [Submitted on 25 Jun 2025 (v1), last revised 25 Mar 2026 (this version, v4)] Title:Recurrent neural network-based robust control systems with regional properties and application to MPC design Authors:Daniele Ravasio, Alessio La Bella, Marcello Farina, Andrea Ballarino View a PDF of the paper titled Recurrent neural network-based robust control systems with regional properties and application to MPC design, by Daniele Ravasio and 3 other authors View PDF HTML (experimental) Abstract: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 va...