### Abstract

This paper presents an adaptive neural network controller (ANNC) that is used to control the speed of a separately excited dc motor deriving a centrifugal pump load and fed from photovoltaic (PV) generator through dc-dc buck-boost converter. The controller is also used to track the maximum power point (MPP) of the PV generator by controlling the converter duty ratio. Such kind of controllers must have two objective functions to perform these two tasks, but in this research the objective function related to the MPP is converted to a constrained for the second objective function by making some approximation in the system equations. An adaptive neural network identifier (ANNI), which emulates the dynamic behavior of the motor system, plays an important role in computing the system Jacobian and hence updating the weights and biases of the ANNC. The weights and biases of both networks are updated on line using BP algorithm with adaptive learning rate. The computation of the adaptive learning rate is based on the value of the speed error through an empirical formula to get faster response with less oscillation and minimum overshoot. The transient response of the motor speed, current and voltage for a step change in the reference speed and the insolation are presented.

Original language | English |
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Title of host publication | Proceedings of the International Joint Conference on Neural Networks |

Pages | 607-612 |

Number of pages | 6 |

Volume | 1 |

Publication status | Published - 2002 |

Externally published | Yes |

Event | 2002 International Joint Conference on Neural Networks (IJCNN '02) - Honolulu, HI Duration: 2002 May 12 → 2002 May 17 |

### Other

Other | 2002 International Joint Conference on Neural Networks (IJCNN '02) |
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City | Honolulu, HI |

Period | 02/5/12 → 02/5/17 |

### Fingerprint

### ASJC Scopus subject areas

- Software

### Cite this

*Proceedings of the International Joint Conference on Neural Networks*(Vol. 1, pp. 607-612)

**The dynamic performance of photovoltaic supplied DC motor fed from DC-DC converter and controlled by neural networks.** / Hussein, Ahmed; Hirasawa, Kotaro; Furuzuki, Takayuki; Murata, Junichi.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Proceedings of the International Joint Conference on Neural Networks.*vol. 1, pp. 607-612, 2002 International Joint Conference on Neural Networks (IJCNN '02), Honolulu, HI, 02/5/12.

}

TY - GEN

T1 - The dynamic performance of photovoltaic supplied DC motor fed from DC-DC converter and controlled by neural networks

AU - Hussein, Ahmed

AU - Hirasawa, Kotaro

AU - Furuzuki, Takayuki

AU - Murata, Junichi

PY - 2002

Y1 - 2002

N2 - This paper presents an adaptive neural network controller (ANNC) that is used to control the speed of a separately excited dc motor deriving a centrifugal pump load and fed from photovoltaic (PV) generator through dc-dc buck-boost converter. The controller is also used to track the maximum power point (MPP) of the PV generator by controlling the converter duty ratio. Such kind of controllers must have two objective functions to perform these two tasks, but in this research the objective function related to the MPP is converted to a constrained for the second objective function by making some approximation in the system equations. An adaptive neural network identifier (ANNI), which emulates the dynamic behavior of the motor system, plays an important role in computing the system Jacobian and hence updating the weights and biases of the ANNC. The weights and biases of both networks are updated on line using BP algorithm with adaptive learning rate. The computation of the adaptive learning rate is based on the value of the speed error through an empirical formula to get faster response with less oscillation and minimum overshoot. The transient response of the motor speed, current and voltage for a step change in the reference speed and the insolation are presented.

AB - This paper presents an adaptive neural network controller (ANNC) that is used to control the speed of a separately excited dc motor deriving a centrifugal pump load and fed from photovoltaic (PV) generator through dc-dc buck-boost converter. The controller is also used to track the maximum power point (MPP) of the PV generator by controlling the converter duty ratio. Such kind of controllers must have two objective functions to perform these two tasks, but in this research the objective function related to the MPP is converted to a constrained for the second objective function by making some approximation in the system equations. An adaptive neural network identifier (ANNI), which emulates the dynamic behavior of the motor system, plays an important role in computing the system Jacobian and hence updating the weights and biases of the ANNC. The weights and biases of both networks are updated on line using BP algorithm with adaptive learning rate. The computation of the adaptive learning rate is based on the value of the speed error through an empirical formula to get faster response with less oscillation and minimum overshoot. The transient response of the motor speed, current and voltage for a step change in the reference speed and the insolation are presented.

UR - http://www.scopus.com/inward/record.url?scp=0036079684&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0036079684&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0036079684

VL - 1

SP - 607

EP - 612

BT - Proceedings of the International Joint Conference on Neural Networks

ER -