Implementation of Self-adaptive System using the Algorithm of Neural Network Learning Gain

  • Lee, Seong-Su (Department of Electric Measurement & Control, Suncheon Campus of Korea Polytechnic) ;
  • Kim, Yong-Wook (Department of Electric Measurement & Control, Namwon Campus of Korea Polytechnic) ;
  • Oh, Hun (Division of Electrical Electronics and Information Engineering, WonKwang University) ;
  • Park, Wal-Seo (Division of Electrical Electronics and Information Engineering, WonKwang University)
  • Published : 2008.06.30

Abstract

The neural network is currently being used throughout numerous control system fields. However, it is not easy to obtain an input-output pattern when the neural network is used for the system of a single feedback controller and it is difficult to obtain satisfactory performance with when the load changes rapidly or disturbance is applied. To resolve these problems, this paper proposes a new mode to implement a neural network controller by installing a real object for control and an algorithm for this, which can replace the existing method of implementing a neural network controller by utilizing activation function at the output node. The real plant object for controlling of this mode implements a simple neural network controller replacing the activation function and provides the error back propagation path to calculate the error at the output node. As the controller is designed using a simple structure neural network, the input-output pattern problem is solved naturally and real-time learning becomes possible through the general error back propagation algorithm. The new algorithm applied neural network controller gives excellent performance for initial and tracking response and shows a robust performance for rapid load change and disturbance, in which the permissible error surpasses the range border. The effect of the proposed control algorithm was verified in a test that controlled the speed of a motor equipped with a high speed computing capable DSP on which the proposed algorithm was loaded.

Keywords

References

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