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Embedded Adaptive Fuzzy Controller Based on Reinforcement Learning for DC Motor with Flexible Shaft

  • Research Article - Computer Engineering and Computer Science
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Abstract

In this paper, we propose an adaptive fuzzy controller in which the scaling factors of the input/output membership functions are adapted in the real time using a Reinforcement Q-Learning algorithm based on a proposed reward function. The proposed controller is implemented practically using an Arduino DUE board to control a DC motor with flexible shaft. The practical results show that the performance of the proposed controller is significantly improved compared with the other controllers. Also, the results show better performance over a wide range of the measurement errors and load disturbances.

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Authors and Affiliations

  1. Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menofia University, Menouf, 32852, Egypt

    A. Aziz Khater, Mohammad El-Bardini & Nabila M. El-Rabaie

Authors
  1. A. Aziz Khater
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  2. Mohammad El-Bardini
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  3. Nabila M. El-Rabaie
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Correspondence to A. Aziz Khater.

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Aziz Khater, A., El-Bardini, M. & El-Rabaie, N.M. Embedded Adaptive Fuzzy Controller Based on Reinforcement Learning for DC Motor with Flexible Shaft. Arab J Sci Eng 40, 2389–2406 (2015). https://doi.org/10.1007/s13369-015-1752-4

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  • DOI: https://doi.org/10.1007/s13369-015-1752-4

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