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SICE Annual Conference (SICE)

Control for swing-up of an inverted pendulum using qubit neural network

Noriaki Kouda, Nobuyuki Matsui, H. Nishimura

Published Jun 26, 2003• 11 citations
Publisher

Abstract

We have proposed a novel neuron model whose states and interactions with other neurons are based on the laws of quantum physics. In our previous works, neural network constricted by this neuron, i.e., qubit neural network, has been shown through various benchmark simulations. As an example of nonholonomic system control, in this report, the swing-up control of inverted pendulum by the qubit neural network is investigated to estimate the efficiencies for more practical applications. For comparison, our qubit neural network and a conventional neural network are evaluated. As a result, we find it is possible for our qubit neural network to swing up and stabilize the inverted pendulum while not for any conventional neural network.

DOI
10.1109/sice.2002.1195253

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This study focused on a controller application of a quantum neural network, using qubit neurons as an information processing unit, and investigated its characteristics when controlling a multiple-input and multiple-output (MIMO) nonlinear system. Using a multilayer quantum neural network, a direct controller that conducts the online training of the quantum neural network was investigated. Computational experiments on the control of a MIMO non-linear discrete-time plant were conducted to evaluate the performance of the quantum neural network-based controller. The experimental results confirmed the effectiveness of the proposed quantum neural network-based controller and elucidated its characteristics.
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