多因素影响下的自适应前照灯系统研究

Proceedings of the 2019 8th International Conference on Networks, Communication and Computing Pub Date : 2019-12-13 DOI:10.1145/3375998.3376007

Liu Shanzhong, Liu Yongbin, Liang Jinhui

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引用次数: 1

摘要

针对自适应前照灯系统(AFS)建模的复杂性，提出了一种通过训练神经网络模型对AFS系统建模的方法。通过对先验知识和实际情况的分析，分别在水平和垂直方向上对两种典型工况进行建模。在MATLAB上建立RBF神经网络对网络进行训练和验证，并在AFS中加入模糊PID控制策略对系统性能进行优化。仿真结果表明，该方法建立的系统模型具有良好的精度，模糊PID控制器可以大大减少前照灯的过度偏转角度，从而提高前照灯的使用寿命和工作精度。

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Research on Adaptive Front-lighting Systems with the influence of multiple factors

Aiming at the complexity of Adaptive Front-lighting Systems(AFS) modeling, a method of modeling AFS system by training neural network model is proposed. Through the analysis of prior knowledge and actual situation, Two typical working conditions are modeled in horizontal and vertical directions. RBF neural network is created on MATLAB to train and verify the network, and the fuzzy PID control strategy is added to AFS to optimize the performance of system. Simulation results show that the system model established by this method has a good precision, the fuzzy PID controller can greatly reduce the excessive deflection angle of headlamp, so the service life and the working accuracy of the headlamp can be improved.

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Proceedings of the 2019 8th International Conference on Networks, Communication and Computing

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