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Mobile Robot Wall-Following Control Using Fuzzy Logic Controller with Improved Differential Search and Reinforcement Learning

Author

Listed:
  • Cheng-Hung Chen

    (Department of Electrical Engineering, National Formosa University, Yunlin 632, Taiwan)

  • Shiou-Yun Jeng

    (Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung 411, Taiwan)

  • Cheng-Jian Lin

    (Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung 411, Taiwan
    College of Intelligence, National Taichung University of Science and Technology, Taichung 404, Taiwan)

Abstract

In this study, a fuzzy logic controller with the reinforcement improved differential search algorithm (FLC_R-IDS) is proposed for solving a mobile robot wall-following control problem. This study uses the reward and punishment mechanisms of reinforcement learning to train the mobile robot wall-following control. The proposed improved differential search algorithm uses parameter adaptation to adjust the control parameters. To improve the exploration of the algorithm, a change in the number of superorganisms is required as it involves a stopover site. This study uses reinforcement learning to guide the behavior of the robot. When the mobile robot satisfies three reward conditions, it gets reward +1. The accumulated reward value is used to evaluate the controller and to replace the next controller training. Experimental results show that, compared with the traditional differential search algorithm and the chaos differential search algorithm, the average error value of the proposed FLC_R-IDS in the three experimental environments is reduced by 12.44%, 22.54% and 25.98%, respectively. Final, the experimental results also show that the real mobile robot using the proposed method can effectively implement the wall-following control.

Suggested Citation

  • Cheng-Hung Chen & Shiou-Yun Jeng & Cheng-Jian Lin, 2020. "Mobile Robot Wall-Following Control Using Fuzzy Logic Controller with Improved Differential Search and Reinforcement Learning," Mathematics, MDPI, vol. 8(8), pages 1-21, July.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:8:p:1254-:d:392722
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    References listed on IDEAS

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    1. Bo Liu, 2014. "Composite Differential Search Algorithm," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-15, August.
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    Cited by:

    1. Guoxin Hua & Fei Wang & Jianhui Zhang & Khalid A. Alattas & Ardashir Mohammadzadeh & Mai The Vu, 2022. "A New Type-3 Fuzzy Predictive Approach for Mobile Robots," Mathematics, MDPI, vol. 10(17), pages 1-16, September.
    2. Man-Wen Tian & Shu-Rong Yan & Ardashir Mohammadzadeh & Jafar Tavoosi & Saleh Mobayen & Rabia Safdar & Wudhichai Assawinchaichote & Mai The Vu & Anton Zhilenkov, 2021. "Stability of Interval Type-3 Fuzzy Controllers for Autonomous Vehicles," Mathematics, MDPI, vol. 9(21), pages 1-17, October.

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