A Hierarchical Fuzzy-neural Multi-model Applied in Nonlinear Systems Identification and Control

DOI

10.2991/isccca.2013.46How to use a DOI?

Keywords

Fuzzy-neural hierarchical multi-model, Recurrent neural networks, Systems identification

Abstract

The paper brings forward a hierarchical fuzzy-neural multi-model with recurrent neural procedural consequent par for systems identification, states estimation and adaptive control of complex nonlinear plants. The parameters and states of the local recurrent neural network models are used for a local direct and indirect adaptive trajectory tracking control systems design. The designed local control laws are coordinated by a fuzzy rule-based control system. The upper level defuzzyfication is performed by a recurrent neural network. The applicability of the proposed intelligent control system is confirmed by simulation examples and by a DC-motor identification and control experimental results. Two main cases of a reference and plant output fuzzyfication are considered—a two membership functions without overlapping and a three membership functions with overlapping. In both cases a good convergent results are obtained.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Feng Ye
AU  - Weimin Qi
PY  - 2013/02
DA  - 2013/02
TI  - A Hierarchical Fuzzy-neural Multi-model Applied in Nonlinear Systems Identification and Control
BT  - Proceedings of the 2nd International Symposium on Computer, Communication, Control and Automation (ISCCCA 2013)
PB  - Atlantis Press
SP  - 183
EP  - 187
SN  - 1951-6851
UR  - https://doi.org/10.2991/isccca.2013.46
DO  - 10.2991/isccca.2013.46
ID  - Ye2013/02
ER  -