2003•The Proceedings of the Dynamics & Design ConferenceOpen access

122 Intelligent Transfer Control to an Unstable Equilibrium Point of Double Inverted Pendulum

Terumasa NARUKAWA, Masaki Takahashi, Kazuo YOSHIDA

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Abstract

The double inverted pendulum has been known as a good example of nonlinear system. In the double inverted pendulum there are four equilibrium points. In this paper, we present a control method for transition between equilibrium points of the double inverted pendulum. The swing-up controller is designed based on energy. The stabilizing controllers are designed on the basis of the linear models at each unstable equilibrium point. Radial Basis Function Neural Network (RBFNN) learned by genetic algorithm is used to integrate the swing-up controller and the stabilizing controllers. The effectiveness of the proposed method is shown by simulation and experimental results.

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The double inverted pendulum has been known as a good example of nonlinear system. In the double inverted pendulum there are four equilibrium points. In this paper, we present a control method for transition between equilibrium points of the double inverted pendulum. The swing-up controller is designed based on energy. The stabilizing controllers are designed on the basis of the linear models at each unstable equilibrium point. Radial Basis Function Neural Network (RBFNN) learned by genetic algorithm is used to integrate the swing-up controller and the stabilizing controllers. The effectiveness of the proposed method is shown by simulation and experimental results.

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Available abstract

The double inverted pendulum has been known as a good example of nonlinear system. In the double inverted pendulum there are four equilibrium points. In this paper, we present a control method for transition between equilibrium points of the double inverted pendulum. The swing-up controller is designed based on energy. The stabilizing controllers are designed on the basis of the linear models at each unstable equilibrium point. Radial Basis Function Neural Network (RBFNN) learned by genetic algorithm is used to integrate the swing-up controller and the stabilizing controllers. The effectiveness of the proposed method is shown by simulation and experimental results.

Key concepts: Inverted pendulum, Double inverted pendulum, Double pendulum, Control theory (sociology), Equilibrium point, Controller (irrigation), Kapitza's pendulum, Swing

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