Model predictive control based deep neural network for dynamic manipulation

Daichi Furuta, Kyo Kutsuzawa, Tetsugaku Okamoto, Sho Sakaino, Toshiaki Tsuji

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

1 Citation (Scopus)

Abstract

This paper proposes a trajectory planning method with neural networks that learn model predictive control for dynamic manipulation. The novelty of this method is that target positions and model parameters are added to the input of the neural network. The proposed method solves the dynamic trajectory planning issues with low calculation cost. This paper shows the effectiveness of the proposed method through demonstrations of a robot turning a pancake over under various parameters. In addition, we verify a performance change with respect to the number of training data.

Original languageEnglish
Title of host publicationProceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5215-5220
Number of pages6
ISBN (Electronic)9781538611272
DOIs
Publication statusPublished - 2017 Dec 15
Externally publishedYes
Event43rd Annual Conference of the IEEE Industrial Electronics Society, IECON 2017 - Beijing, China
Duration: 2017 Oct 292017 Nov 1

Publication series

NameProceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society
Volume2017-January

Conference

Conference43rd Annual Conference of the IEEE Industrial Electronics Society, IECON 2017
CountryChina
CityBeijing
Period17/10/2917/11/1

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Control and Optimization
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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  • Cite this

    Furuta, D., Kutsuzawa, K., Okamoto, T., Sakaino, S., & Tsuji, T. (2017). Model predictive control based deep neural network for dynamic manipulation. In Proceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society (pp. 5215-5220). (Proceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society; Vol. 2017-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IECON.2017.8216902