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FBCF: A Fuzzy-Based Brake-Assisting Control Function for Rail Vehicles Using Type-1 and Type-2 Fuzzy Inference Models

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Advances in Internet, Data & Web Technologies (EIDWT 2023)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 161))

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Abstract

In railway networks, the driver expertise such as accuracy, physical and experience condition has a major impact on the quality of comfort for rail car passengers. In our previous work, we have proposed a Fuzzy-based brake-assisting function considering velocity, brake level and environment to assist the rail drivers. In this paper, we consider the gravitational acceleration level and slope angle as additional parameters to control the brake-assisting function. We present a comparison study for Type-2 and Type-1 inference models. The proposed system, called Fuzzy-based Brake-assisting Control Function (FBCF), provides intelligent braking for passengers and train drivers by considering five input parameters. The simulation results show that the proposed FBCF provides soft brake assistance considering various situations created by the train and rail environment in order that the passengers have a comfortable feeling.

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Authors and Affiliations

  1. Graduate School of Engineering, Fukuoka Institute of Technology, 3-30-1 Wajiro-higashi, Higashi-ku, Fukuoka, 811-0295, Japan

    Mitsuki Tsuneyoshi

  2. Department of Information and Communication Engineering, Fukuoka Institute of Technology, 3-30-1 Wajiro-higashi, Higashi-ku, Fukuoka, 811-0295, Japan

    Makoto Ikeda & Leonard Barolli

Authors
  1. Mitsuki Tsuneyoshi
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  2. Makoto Ikeda
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  3. Leonard Barolli
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Corresponding author

Correspondence to Makoto Ikeda .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

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Tsuneyoshi, M., Ikeda, M., Barolli, L. (2023). FBCF: A Fuzzy-Based Brake-Assisting Control Function for Rail Vehicles Using Type-1 and Type-2 Fuzzy Inference Models. In: Barolli, L. (eds) Advances in Internet, Data & Web Technologies. EIDWT 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 161. Springer, Cham. https://doi.org/10.1007/978-3-031-26281-4_44

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