Development of energy efficient drive for ventilation system using recurrent neural network

Prince, Ananda Shankar Hati, Prasun Chakrabarti, Jemal H. Abawajy, Ng Wee Keong

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

This research article corroborates the working of a model reference adaptive model (MRAS) with fractional-order proportional-integral (FOPIλ)-based encoderless speed control approach for ventilation system drive using recurrent neural network (RNN) for the low- and medium-range operation. The purpose of this study is to minimize the energy loss due to fluctuations and variation in the rotor speed and also find the optimum values of FOPIλ by using a recurrent neural network to enhance the overall implementation of the system. In this perspective, the low-speed execution of MRAS is poor due to the existence of a pure integral and derivative parameter. Towards enhancement of the performance at speed region, a MRAS method with RNN is used. The network is trained using the Levenberg–Marquardt (LM) algorithm, and FOPIλ control method is used for tuning the gain of proportional-integral of speed and current controller of the encoderless speed control of the ventilation drive. The presented RNN speed estimator with FOPIλ controller has shown better performance and stability in transitory and stable operation as well as it also provides an enhancement in the overall efficiency of the ventilation drive. The validation of the presented algorithm is detailed experiments on a fully digitized 5.5 kW ventilation system using the Lab VIEW interface.

Original languageEnglish
Pages (from-to)8659-8668
Number of pages10
JournalNeural Computing and Applications
Volume33
Issue number14
DOIs
StatePublished - Jul 2021

Keywords

  • FOPI
  • Recurrent neural network
  • Sensor-less speed control
  • Ventilation system

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