Control for stochastic tracking error minimization based on state entropy with neural network

Hayato Maki, Seiichiro Katsura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

It is well known that when the disturbance in the nonlinear system has whiteness, the error between the output of the system and the desired output does not show whiteness. Considering the adaptive system, the mean square evaluation function is often not sufficient for this problem. In recent years, entropy has been attracting attention as an evaluation function changing to the mean square criterion. Beginning with entropy of Shannon, its characteristics are related to higher-order statistics. When entropy is minimized, all moments of error are constrained. Especially in dynamic modeling, the entropy's criterion is more robust than that of MSE. In this research, we focus on correntrop, which has expanded Renyi's entropy more generally, consider controlling and updating the controller by the neural network. For state estimation of correntropy, state change of entropy of error is taken into account by explicitly using state quantity of control rather than time series data set. We confirm by simulation that the proposed method makes the probability density function of the error sharper.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Industrial Technology, ICIT 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages105-110
Number of pages6
Volume2018-February
ISBN (Electronic)9781509059492
DOIs
Publication statusPublished - 2018 Apr 27
Event19th IEEE International Conference on Industrial Technology, ICIT 2018 - Lyon, France
Duration: 2018 Feb 192018 Feb 22

Other

Other19th IEEE International Conference on Industrial Technology, ICIT 2018
CountryFrance
CityLyon
Period18/2/1918/2/22

Fingerprint

Entropy
Neural networks
Function evaluation
Higher order statistics
Adaptive systems
State estimation
Probability density function
Nonlinear systems
Time series
Controllers

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

Maki, H., & Katsura, S. (2018). Control for stochastic tracking error minimization based on state entropy with neural network. In Proceedings - 2018 IEEE International Conference on Industrial Technology, ICIT 2018 (Vol. 2018-February, pp. 105-110). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIT.2018.8352160

Control for stochastic tracking error minimization based on state entropy with neural network. / Maki, Hayato; Katsura, Seiichiro.

Proceedings - 2018 IEEE International Conference on Industrial Technology, ICIT 2018. Vol. 2018-February Institute of Electrical and Electronics Engineers Inc., 2018. p. 105-110.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Maki, H & Katsura, S 2018, Control for stochastic tracking error minimization based on state entropy with neural network. in Proceedings - 2018 IEEE International Conference on Industrial Technology, ICIT 2018. vol. 2018-February, Institute of Electrical and Electronics Engineers Inc., pp. 105-110, 19th IEEE International Conference on Industrial Technology, ICIT 2018, Lyon, France, 18/2/19. https://doi.org/10.1109/ICIT.2018.8352160
Maki H, Katsura S. Control for stochastic tracking error minimization based on state entropy with neural network. In Proceedings - 2018 IEEE International Conference on Industrial Technology, ICIT 2018. Vol. 2018-February. Institute of Electrical and Electronics Engineers Inc. 2018. p. 105-110 https://doi.org/10.1109/ICIT.2018.8352160
Maki, Hayato ; Katsura, Seiichiro. / Control for stochastic tracking error minimization based on state entropy with neural network. Proceedings - 2018 IEEE International Conference on Industrial Technology, ICIT 2018. Vol. 2018-February Institute of Electrical and Electronics Engineers Inc., 2018. pp. 105-110
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