Nonlinear MPC design for incrementally ISS systems with application to GRU networks
Abstract
This brief addresses the design of a Nonlinear Model Predictive Control (NMPC) strategy for exponentially incremental Input-to-State Stable (ISS) systems. In particular, a novel formulation is devised, which does not necessitate the onerous computation of terminal ingredients, but rather relies on the explicit definition of a minimum prediction horizon ensuring closed-loop stability. The designed methodology is particularly suited for the control of systems learned by Recurrent Neural Networks (RNNs), which are known for their enhanced modeling capabilities and for which the incremental ISS properties can be studied thanks to simple algebraic conditions. The approach is applied to Gated Recurrent Unit (GRU) networks, providing also a method for the design of a tailored state observer with convergence guarantees. The resulting control architecture is tested on a benchmark system, demonstrating its good control performances and efficient applicability.
Keywords
Cite
@article{arxiv.2309.16428,
title = {Nonlinear MPC design for incrementally ISS systems with application to GRU networks},
author = {Fabio Bonassi and Alessio La Bella and Marcello Farina and Riccardo Scattolini},
journal= {arXiv preprint arXiv:2309.16428},
year = {2023}
}
Comments
{\copyright} 2023. This manuscript version is made available under the CC-BY-NC-ND 4.0 license (https://creativecommons.org/licenses/by-nc-nd/4.0/). This manuscript has been accepted for publication at Elsevier Automatica. Please cite the published article instead of this manuscript