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Research on the Control System of Two-wheel Balance Vehicles on the Basis of Fuzzy Neural Network

Published: 01 February 2021 Publication History

Abstract

A control method on the basis of fuzzy RBF neural network PID is put forward to solve the problem that balance vehicles can not better adapt to users of different body types and road surfaces of different complexity because of their fixed control parameters. Firstly, the mathematical model of balance vehicles is established with the Newtonian mechanics so as to obtain a dynamic equation of this system. Secondly, the fuzzy control and RBF neural network are combined to obtain a fuzzy neural network control system that can dynamically adjust the parameters. The control system of the balance vehicles is built on the simulink platform of MATLAB and simulated with the following methods: the fuzzy RBF neural network PID control, fuzzy PID control and traditional PID control methods. Last, the self-balancing and antijamming experiments are carried out on the balance vehicle experimental platform. After comparing the experimental results, it can be found that the first method represents better control performance than the later two ones, because it enjoys smaller overshoot, better adaptability and anti-interference, and can be applied to different users and in varied scenes.

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  1. Research on the Control System of Two-wheel Balance Vehicles on the Basis of Fuzzy Neural Network

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    EITCE '20: Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering
    November 2020
    1202 pages
    ISBN:9781450387811
    DOI:10.1145/3443467
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 01 February 2021

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    Author Tags

    1. Anti-interference
    2. Fuzzy control
    3. PID control
    4. RBF neural network
    5. Two-wheel balance vehicles

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    EITCE '20 Paper Acceptance Rate 214 of 441 submissions, 49%;
    Overall Acceptance Rate 508 of 972 submissions, 52%

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