Robustness and fault-tolerance of cubic neural network intelligent control method - Comparison with sliding mode control

Masaki Takahashi, T. Narukawa, K. Yoshida

研究成果: Conference contribution

抄録

This study aims at establishing a robust intelligent control method with higher control performance and wider applicable region by extending the cubic neural network (CNN) intelligent control method which consists of multilevel parallel processing on different degrees of abstraction. In particular, this study deals with a nonlinear and failure-proof control problem. In the control, the dynamical energy principle is embedded into an integrator neural network of the integrated CNN (ICNN). The proposed ICNN is applied to a control problem of a swung up and inverted pendulum mounted on a cart for the case that arbitrary initial condition of pendulum angle. In order to confirm the performance of the ICNN controller, computer simulations and experiments using a real apparatus were carried out for the cases of parameter variation and sensor failure. As a result, it is demonstrated that the ICNN controller can stand up the pendulum taking into account the cart position limit at abnormal simulations. Then, the robustness and the fault-tolerance of the proposed CNN controller were verified in comparison with the sliding mode control technique.

本文言語English
ホスト出版物のタイトルIEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM
出版社Institute of Electrical and Electronics Engineers Inc.
ページ17-22
ページ数6
1
ISBN(印刷版)0780377591
DOI
出版ステータスPublished - 2003
イベント2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2003 - Kobe, Japan
継続期間: 2003 7月 202003 7月 24

Other

Other2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2003
国/地域Japan
CityKobe
Period03/7/2003/7/24

ASJC Scopus subject areas

  • 電子工学および電気工学
  • 制御およびシステム工学
  • コンピュータ サイエンスの応用
  • ソフトウェア

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