Proceedings of the 2016 4th International Conference on Advanced Materials and Information Technology Processing (AMITP 2016)

The study of PSO-RBF neural network generalized predictive control strategy in unit plant

Authors
Hui Wang, Hujun Ling, Lei Pan
Corresponding Author
Hui Wang
Available Online September 2016.
DOI
https://doi.org/10.2991/amitp-16.2016.14How to use a DOI?
Keywords
particle swarm optimization algorithm RBF neural network generalized predictive control generating unit
Abstract
Unit coordinated control in thermal power plants is a system which is complex,nonlinear and is difficulty to establish accurate model, So it is hard to make system gain optimum running effect with conventional control strategy. PSO-RBF neural network is used to identify the mathematical model of coordinated control system and acts as a predictive model in generalized predictive control strategy, which is to achieves predictive control with online rolling optimization and real time feedback revision. Simulation results show that it has a strong robustness when the load condition changes,or big lag affects.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Cite this article

TY  - CONF
AU  - Hui Wang
AU  - Hujun Ling
AU  - Lei Pan
PY  - 2016/09
DA  - 2016/09
TI  - The study of PSO-RBF neural network generalized predictive control strategy in unit plant
BT  - Proceedings of the 2016 4th International Conference on Advanced Materials and Information Technology Processing (AMITP 2016)
PB  - Atlantis Press
SP  - 72
EP  - 76
SN  - 2352-538X
UR  - https://doi.org/10.2991/amitp-16.2016.14
DO  - https://doi.org/10.2991/amitp-16.2016.14
ID  - Wang2016/09
ER  -