In this paper the construction of a neural-network based closed-loop control of a discontinuous capsule drive is analyzed. The foundation of the designed controller is an optimized open-loop control function. A neural network is used to determine the dependence between the output of the open-loop controller and the state of the system. Robustness of the neural controller with respect to variation of parameters of the controlled system is analyzed and compared with the original optimized open-loop control. It is expected that the presented method can facilitate the construction of closed-loop controllers for which alternative methods are not effective, such as non-smooth or discontinuous ones.

Optimization of the closed-loop controller of a discontinuous capsule drive using a neural network / Zarychta, S; Balcerzak, M; Denysenko, V; Stefanski, A; Dabrowski, A; Lenci, S. - In: MECCANICA. - ISSN 0025-6455. - STAMPA. - 58:2-3(2023), pp. 537-553. [10.1007/s11012-023-01639-4]

Optimization of the closed-loop controller of a discontinuous capsule drive using a neural network

Lenci, S
2023-01-01

Abstract

In this paper the construction of a neural-network based closed-loop control of a discontinuous capsule drive is analyzed. The foundation of the designed controller is an optimized open-loop control function. A neural network is used to determine the dependence between the output of the open-loop controller and the state of the system. Robustness of the neural controller with respect to variation of parameters of the controlled system is analyzed and compared with the original optimized open-loop control. It is expected that the presented method can facilitate the construction of closed-loop controllers for which alternative methods are not effective, such as non-smooth or discontinuous ones.
2023
File in questo prodotto:
File Dimensione Formato  
Lenci_2023_06.pdf

accesso aperto

Descrizione: articolo
Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza d'uso: Creative commons
Dimensione 2.54 MB
Formato Adobe PDF
2.54 MB 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/11566/314099
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 7
  • ???jsp.display-item.citation.isi??? 6
social impact