This paper presents a simple technique for the automatic tuning of classical regulators, based on a structural identification method. The core of the proposed approach is a neural pattern classifier, which selects an appropriate model structure for the system to be controlled, starting from a simplified representation of its unit step response. In addition, simple model-specific regulator synthesis rules are suggested which benefit from the available knowledge on the process model structure. The technique is compared with the classical IMC autotuning approach. A real application example, based on a laboratory distillation column, is also presented.

Model-specific autotuning of classical regulators: a neural approach to structural identification

LEVA, ALBERTO;PIRODDI, LUIGI
1996

Abstract

This paper presents a simple technique for the automatic tuning of classical regulators, based on a structural identification method. The core of the proposed approach is a neural pattern classifier, which selects an appropriate model structure for the system to be controlled, starting from a simplified representation of its unit step response. In addition, simple model-specific regulator synthesis rules are suggested which benefit from the available knowledge on the process model structure. The technique is compared with the classical IMC autotuning approach. A real application example, based on a laboratory distillation column, is also presented.
Autotuners, neural networks, system identification, industrial control, process control
File in questo prodotto:
File Dimensione Formato  
LevaPiroddi-CEP-1996.pdf

Accesso riservato

: Post-Print (DRAFT o Author’s Accepted Manuscript-AAM)
Dimensione 1.12 MB
Formato Adobe PDF
1.12 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/11311/559430
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 14
  • ???jsp.display-item.citation.isi??? 14
social impact