Sensors and Materials
ISSN 0914-4935

Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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Sensors and Materials,
Volume 29, Number 6(1) (2017)

Copyright(C) MYU K.K. All Rights Reserved.
pp. 699-711
S&M1361
https://doi.org/10.18494/SAM.2017.1468
Published: June 7, 2017

Design of Quaternion-Neural-Network-Based Self-Tuning Control Systems [PDF]

Kazuhiko Takahashi, Yusuke Hasegawa, and Masafumi Hashimoto

(Received September 12, 2016; Accepted January 5, 2017)

Keywords: quaternion neural network, self-tuning controller, PID controller, nonlinear plant, reference model

In this study, we investigate the control performance of an adaptive controller using a multilayer quaternion neural network. The control system is a self-tuning controller, the control parameters of which are tuned online by the quaternion neural network to track plant output to follow the desired output generated by a reference model. A proportional–integral–derivative (PID) controller is used as a conventional controller, the parameters of which are tuned by the quaternion neural network. Computational experiments to control a single-input single-output (SISO) discrete-time nonlinear plant are conducted to evaluate the capability and characteristics of the quaternion-neural-networkbased self-tuning PID controller. Experimental results show the feasibility and effectiveness of the proposed controller.

Corresponding author: Kazuhiko Takahashi


Cite this article
Kazuhiko Takahashi, Yusuke Hasegawa, and Masafumi Hashimoto, Design of Quaternion-Neural-Network-Based Self-Tuning Control Systems, Sens. Mater., Vol. 29, No. 6, 2017, p. 699-711.


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