Altitude control of heavy-lift hexacopter using direct inverse control based on elman recurrent neural network

Bhakti Yudho Suprapto, M. Ary Heryanto, Herwin Suprijono, Benyamin Kusumo Putro

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

This paper proposes the use of Direct Inverse Control (DIC) with Elman Recurrent Neural Network (ERNN) learning algorithm for the altitude control of a heavy-lift hexacopter. The study was conducted analytically using the real flight data obtained from real plant experiment. The results showed that the ERNN can successfully control the altitude of the heavy-lift hexacopter, where the response generated by the DIC system was in good agreement with the test data with low error. Furthermore, the proposed DIC system can also control the attitude, e.g. roll, pitch and yaw of the hexacopter which are also crucial for the hexacopter movement control.

Original languageEnglish
Title of host publicationProceedings of the 8th International Conference on Computer Modeling and Simulation, ICCMS 2017
PublisherAssociation for Computing Machinery
Pages135-140
Number of pages6
ISBN (Electronic)9781450348164
DOIs
Publication statusPublished - 20 Jan 2017
Event8th International Conference on Computer Modeling and Simulation, ICCMS 2017 - Canberra, Australia
Duration: 20 Jan 201723 Jan 2017

Publication series

NameACM International Conference Proceeding Series
VolumePart F128047

Conference

Conference8th International Conference on Computer Modeling and Simulation, ICCMS 2017
CountryAustralia
CityCanberra
Period20/01/1723/01/17

Keywords

  • Altitude control
  • DIC
  • Elman recurrent neural networks
  • Heavy-lift hexacopter
  • Neural networks

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  • Cite this

    Suprapto, B. Y., Heryanto, M. A., Suprijono, H., & Putro, B. K. (2017). Altitude control of heavy-lift hexacopter using direct inverse control based on elman recurrent neural network. In Proceedings of the 8th International Conference on Computer Modeling and Simulation, ICCMS 2017 (pp. 135-140). (ACM International Conference Proceeding Series; Vol. Part F128047). Association for Computing Machinery. https://doi.org/10.1145/3036331.3036354