Proceedings of the 2017 International Conference on Material Science, Energy and Environmental Engineering (MSEEE 2017)

Model Reference Self-Adaptive Control Systems based on Single Neurons

Authors
Xiaobin Liu, Mengda Li
Corresponding Author
Xiaobin Liu
Available Online August 2017.
DOI
https://doi.org/10.2991/mseee-17.2017.5How to use a DOI?
Keywords
Model reference, Vector control, Single neuron.
Abstract
This paper presents a model based on single neuron for adaptive control. For the weakness of traditional PID controllers and complex neural networks, based on the model reference adaptive control, this text using a single neuron instead of a complex neural network, choose the linear function as the reference model, and take the velocity change into the error function, and built the control system with the structure of magnetic chain open-loop and rotate speed close-loop. And then, the control system simulation model based on this control method was established by asynchronous motor, and applied the TMS320 series DSP to set up experimental control system. The simulation and experimental results show that the controller robust is strong, with adaptive characteristics of time-varying parameters and load.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2017 International Conference on Material Science, Energy and Environmental Engineering (MSEEE 2017)
Part of series
Advances in Engineering Research
Publication Date
August 2017
ISBN
978-94-6252-377-7
ISSN
2352-5401
DOI
https://doi.org/10.2991/mseee-17.2017.5How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Xiaobin Liu
AU  - Mengda Li
PY  - 2017/08
DA  - 2017/08
TI  - Model Reference Self-Adaptive Control Systems based on Single Neurons
BT  - 2017 International Conference on Material Science, Energy and Environmental Engineering (MSEEE 2017)
PB  - Atlantis Press
SP  - 23
EP  - 27
SN  - 2352-5401
UR  - https://doi.org/10.2991/mseee-17.2017.5
DO  - https://doi.org/10.2991/mseee-17.2017.5
ID  - Liu2017/08
ER  -