A robust control method for a PV-supplied DC motor using universal learning networks

Ahmed Hussein, Kotaro Hirasawa, Takayuki Furuzuki

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

In this paper, a new robust control method and its application to a photovoltaic (PV) supplied, separately excited DC motor loaded with a constant torque is discussed. The robust controller is designed against the load torque changes by using the first and second ordered derivatives of the universal learning networks (ULNs). These derivatives are calculated using the forward propagation algorithm, which is considered as an extended version of real time recurrent learning (RTRL). In this application, two ULNs are used: The first is the ULN identifier trained offline to emulate the dynamic performance of the DC motor system. The second is the ULN controller, which is trained online not only to make the motor speed follow a selected reference signal, but also to make the overall system operate at the maximum power point of the PV source. To investigate the effectiveness of the proposed robust control method, the simulation is carried out at four different values of the robustness coefficient γ in two different stages: The training stage, in which the simulation is done for a constant load torque. And the control stage, in which the controller performance is obtained when the load torque is changed. The simulation results showed that the robustness of the control system is improved although the motor load torque at the control stage is different from that at the training stage.

Original languageEnglish
Pages (from-to)771-780
Number of pages10
JournalSolar Energy
Volume76
Issue number6
DOIs
Publication statusPublished - 2004 Jun

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Keywords

  • Neural networks
  • Photovoltaic generators
  • Robust control
  • Universal learning networks

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Electronic, Optical and Magnetic Materials
  • Surfaces, Coatings and Films

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