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

Ahmed Hussein, Kotaro Hirasawa, Jinglu Hu

Research output: Contribution to journalConference articlepeer-review

4 Citations (Scopus)


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)
Issue number16
Publication statusPublished - 2003
Event13th IFAC Symposium on System Identification, SYSID 2003 - Rotterdam, Netherlands
Duration: 2003 Aug 272003 Aug 29


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

ASJC Scopus subject areas

  • Control and Systems Engineering


Dive into the research topics of 'Identification and Control of a PV-Supplied Separately Excited DC Motor Using Universal Learning Networks'. Together they form a unique fingerprint.

Cite this