Intelligent control of integrated cubic neural network for robustness and fault-tolerance

Masaki Takahashi, Terumasa Narukawa, Kazuo Yoshida

研究成果: Conference article

抜粋

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 this study, an integrated CNN (ICNN) intelligent control method for nonlinear systems is proposed. In the method, an integrator neural network acquires suitable switching of several CNN controllers for a different local objective based on an evaluation of system states. 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, computer simulations and experiments using a real apparatus were carried out for the cases of parameter variation and sensor failure. As a result, it was demonstrated that the ICNN controller can stand up the pendulum taking into account the cart position limit at abnormal situations. Then, the robustness and the fault-tolerance of the ICNN were verified in comparison with the sliding mode control technique.

元の言語English
ページ(範囲)493-498
ページ数6
ジャーナルIFAC Proceedings Volumes (IFAC-PapersOnline)
37
発行部数12
DOI
出版物ステータスPublished - 2004 1 1
イベント2004 IFAC Workshop on Adaptation and Learning in Control and Signal Processing, ALCOSP 2004 and IFAC Workshop on Periodic Control Systems, PSYCO 2004 - Yokohama, Japan
継続期間: 2004 8 302004 9 1

ASJC Scopus subject areas

  • Control and Systems Engineering

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