Identification and Control of a PV-Supplied Separately Excited DC Motor Using Universal Learning Networks

Ahmed Hussein, Kotaro Hirasawa, Takayuki Furuzuki

Research output: Contribution to journalConference article

1 Citation (Scopus)

Abstract

This paper describes the use of Universal Learning Networks (ULNs) in the identification and control of a separately excited de motor loaded with a centrifugal pump and fed from Photovoltaic (PV) generator via dc-dc buck-boost converter. The Universal Learning Network Identifier (ULNI) is trained offline using the forward propagation algorithm to emulate the dynamic behavior of the de motor system. Then this identifier is used, instead of the motor system, for the online training of the Universal Learning Network Controller (ULNC). As a result, the motor speed can follow all arbitrarily selected reference signal. Furthermore, the overall system call operate at the Maximum Power Point (MPP) of the PV generator. which is the optimal operating point. The simulation results showed a good performance for the identifier and the controller as well.

Original languageEnglish
Pages (from-to)1411-1416
Number of pages6
JournalIFAC Proceedings Volumes (IFAC-PapersOnline)
Volume36
Issue number16
DOIs
Publication statusPublished - 2003 Jan 1
Event13th IFAC Symposium on System Identification, SYSID 2003 - Rotterdam, Netherlands
Duration: 2003 Aug 272003 Aug 29

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DC motors
Controllers
Centrifugal pumps

Keywords

  • DC Motors
  • Neural Networks
  • Power Converters
  • PV Generators

ASJC Scopus subject areas

  • Control and Systems Engineering

Cite this

Identification and Control of a PV-Supplied Separately Excited DC Motor Using Universal Learning Networks. / Hussein, Ahmed; Hirasawa, Kotaro; Furuzuki, Takayuki.

In: IFAC Proceedings Volumes (IFAC-PapersOnline), Vol. 36, No. 16, 01.01.2003, p. 1411-1416.

Research output: Contribution to journalConference article

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