Intelligent stabilization control to arbitrary equilibrium point of double inverted pendulum

Masaki Takahashi, Terumasa Narukawa, Kazuo Yoshida

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

This study aims at establishing a robust and effective intelligent control method for nonlinear and complicated systems by extending the integrated Cubic Neural Network (CNN). In the method, an integrator neural network acquires optimum switching and integration of several controllers for a different local purpose by calculating the fitness based on the system object using the genetic algorithm. The proposed method is applied to an equilibrium point transfer and stabilization control of a double pendulum that possesses four equilibrium points, namely Down-Down, Down-Up, Up-Down and Up-Up points. In order to verify the effectiveness of the proposed method, simulations and experiments were carried out. As a result, it was demonstrated that the integrated intelligent controllers can transfer and stabilize the double pendulum from the arbitrary equilibrium points to arbitrary one without touching the cart position limit. In addition, by taking account of energy variation, the double pendulum was transferred from Up-Down to Up-Up and was stabilized without falling down.

Original languageEnglish
Pages (from-to)15-22
Number of pages8
JournalNippon Kikai Gakkai Ronbunshu, C Hen/Transactions of the Japan Society of Mechanical Engineers, Part C
Volume70
Issue number1
Publication statusPublished - 2004 Jan 1

Keywords

  • Cubic neural network
  • Double pendulum
  • Genetic algorithm
  • Intelligent control
  • Nonlinear control

ASJC Scopus subject areas

  • Mechanics of Materials
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering

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