SCADA system of DC motor with implementation of fuzzy logic controller on neural network

Authors:
Jui-Hong Horng
Affiliations:
Department of Mechanical and Marine Engineering, National Taiwan Ocean University, Keelung 202, Taiwan, ROC
Venue:
Advances in Engineering Software
Year:
2002

Citing 6
Cited 1

On the approximate realization of continuous mappings by neural networks

Neural Networks
Indirect adaptive control of a class of unknown nonlinear discrete-time systems using hybrid neural networks

Systems Analysis Modelling Simulation
Neural adaptive tracking control of a DC motor

Information Sciences: an International Journal
Supervisory Control and Data Acquisition

Supervisory Control and Data Acquisition
Identification and control of dynamic systems using recurrent fuzzy neural networks

IEEE Transactions on Fuzzy Systems
Hybrid supervisory control using recurrent fuzzy neural network for tracking periodic inputs

IEEE Transactions on Neural Networks

Genetically generated double-level fuzzy controller with a fuzzy adjustment strategy

Proceedings of the 9th annual conference on Genetic and evolutionary computation

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Abstract

In this paper, a supervisory control and data acquisition system of DC motor with implementation of fuzzy logic controller (FLC) on neural network (NN) is presented. We successfully avoid complex data processing of fuzzy logic in the proposed scheme. After designed a FLC for controlling the motor speed, a NN is trained to learn the input-output relationship of FLC. The tasks of sampling and acquiring the input signals, process of the input data, and output of the voltage are commanded by using LabV1EW. Finally, the experimental results are provided to confirm the performance and effectiveness of the proposed control approach.