Robustness and fault-tolerance of cubic neural network intelligent control method (comparison with sliding mode control)

Masaki Takahashi, Terumasa Narukawa, Kazuo Yoshida

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

This study aims at establishing a robust intelligent control method with higher control performance and wider application region. In particular, this study deals with a nonlinear and failure-proof control problem. In this study, the dynamical energy principle is embedded into the integrator of Cubic Neural Network (CNN) that consists of multilevel parallel processing on different degrees of abstraction. The proposed CNN is applied to a control problem of a swung up and inverted pendulum. In order to confirm the effectiveness of the integrated CNN controllers, we carried out simulations and experiments with parameter variation and sensor failure. As a result of simulation and experiment, it was demonstrated that the integrated CNN controllers can stand up the pendulum taking into account the cart position limit at abnormal situations. Then, the robustness and the fault-tolerance of the proposed CNN controllers is confirmed by comparing sliding mode control techniques.

Original languageEnglish
Pages (from-to)1579-1586
Number of pages8
JournalNippon Kikai Gakkai Ronbunshu, C Hen/Transactions of the Japan Society of Mechanical Engineers, Part C
Volume69
Issue number6
Publication statusPublished - 2003 Jun

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Intelligent control
Sliding mode control
Fault tolerance
Neural networks
Pendulums
Controllers
Robust control
Experiments
Sensors
Processing

Keywords

  • Fault-Tolerance
  • Intelligent Control
  • Multi-Purpose Control
  • Nonlinear Control
  • Pendulum
  • Robustness

ASJC Scopus subject areas

  • Mechanical Engineering

Cite this

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