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Truncation Error Correction for Dynamic Matrix Control Based on RBF Neural Network

  • Youming WangEmail author
  • Jugang Li
  • Feng Ji
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 891)

Abstract

In this paper, a correction method for truncation error of dynamic matrix control is studied. A truncation error correction method is designed by using the law of the dynamic change of control system. Firstly, the predictive initial value is calculated using different compensation parameters and the difference between the last two components of the predicted initial value vector is obtained. Secondly, the compensation parameter is fitted based on RBF neural network. Finally, the compensation parameter is used to correct error during feedback correction. Numerical experiments show that the proposed method can improve the overshoot and hysteresis characteristics of the system.

Keywords

Dynamic matrix control Truncation error RBF neural network 

Notes

Acknowledgements

This work was supported by the Key Research and Development Program of Shaanxi Province of China (2017GY-168) and the New Star Team of Xi’an University of Posts and Telecommunications. It was also supported by the graduate student innovation fund of Xi’an University of Posts and Telecommunications (CXL2016-20) and the Department of Education Shaanxi Province, China, under Grant 2013JK1023.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Xi’an University of Posts and TelecommunicationsXi’anChina
  2. 2.Shaanxi Environmental Protection Research Institute, Co.Xi’anChina

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