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.(c) 2023 Elsevier Ltd. All rights reserved.

Nonlinear MPC design for incrementally ISS systems with application to GRU networks

Bonassi, Fabio;La Bella, Alessio;Farina, Marcello;Scattolini, Riccardo
2024-01-01

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.(c) 2023 Elsevier Ltd. All rights reserved.
2024
Nonlinear model predictive control
Recurrent neural networks
Gated recurrent units
File in questo prodotto:
File Dimensione Formato  
1-s2.0-S0005109823005484-main.pdf

Accesso riservato

: Publisher’s version
Dimensione 700.28 kB
Formato Adobe PDF
700.28 kB Adobe PDF   Visualizza/Apri
11311-1260618 Bonassi.pdf

embargo fino al 31/10/2024

: Post-Print (DRAFT o Author’s Accepted Manuscript-AAM)
Dimensione 625.96 kB
Formato Adobe PDF
625.96 kB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1260618
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 6
  • ???jsp.display-item.citation.isi??? ND
social impact