Parameter Optimization with Input/Output Data via DE for Adaptive Control System with Neural Network

Authors
Taro [email protected]
Department of Control Engineering, Natl. Inst. of Tech., Maizuru College, 234 Shiroya, Maizuru, Kyoto 625-8511, Japan
Ikuro [email protected]
Graduate School of Science and Technology, Kumamoto University, 2-39-1 Kurokami Chuo-ku, Kumamoto 860-8555, Japan
Available Online 30 June 2018.
DOI
https://doi.org/10.2991/jrnal.2018.5.1.5
Keywords
Adaptive Control; ASPR; PFC; Neural network; Differential evolution
Abstract
In this paper, adaptive control system with neural network (NN) will be designed. At the beginning, parallel feedforward compensator (PFC) will be designed by using one-shot experimental data of controlled system via differential evolution (DE). From the obtained PFC and the ideal almost strictly positive real (ASPR) model, nominal model of controlled system can be obtained. Then, parameters of adjust law for NN will be optimized by using obtained nominal model via DE.

Copyright
Copyright © 2018, the Authors. Published by ALife Robotics Corp. Ltd.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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