基于模糊与遗传逼近策略的轮胎预设定值充放气控制

计算机应用 ›› 2013, Vol. 33 ›› Issue (11): 3305-3308.

基于模糊与遗传逼近策略的轮胎预设定值充放气控制

潘小波

马鞍山职业技术学院电气工程系，安徽马鞍山 243031

收稿日期:2013-05-24 修回日期:2013-07-24 发布日期:2013-12-04 出版日期:2013-11-01
通讯作者: 潘小波
作者简介:潘小波（1972-），男，安徽桐城人，副教授，硕士，主要研究方向：嵌入式系统软硬件设计、检测技术与装置研究与设计。

Tire preset value inflation or deflation control based on fuzzy and genetic approximation strategy

PAN Xiaobo

Department of Electrical Engineering, Maanshan Technical College, Maanshan Anhui 243031,China

Received:2013-05-24 Revised:2013-07-24 Online:2013-12-04 Published:2013-11-01
Contact: PAN Xiaobo

摘要/Abstract

摘要： 影响轮胎充放气的因素比较复杂，为了简便、准确地对轮胎进行预设定值充放气，提出一种基于模糊与遗传逼近策略的轮胎预设定值充放气控制方法。该方法把整个充放气过程分成两步：首先根据预设定值和当前胎压与预设定值的差,模糊推理出充放气的时间进行预充放；然后依次遗传前面的充放气速率逐步逼近至预设定值。实验结果表明，在不同的充放气条件下利用该方法均能简便、准确地充放气到预设定值，精度可达±0.04bar，适用于各种自动轮胎充放气设备，且简便、高效、实用。

关键词: 胎压, 预设定值, 单片机, 模糊控制, 遗传逼近

Abstract: The influence factors of tire inflation or deflation are very complicated. In order to make tire to be conveniently and accurately inflated or deflated to the preset pressure value, a tire inflating or deflating control method based on fuzzy and genetic approximation was proposed in this paper. The entire inflating or deflating process was divided into two steps. Firstly the inflating or deflating time was reasoned by fuzzy reasoning according to the preset value and the difference between the current tire pressure and the preset value, and tire was preliminarily inflated or deflated according to this time. Then tire gradually approached the preset value by inheriting last inflating or deflating rate. The experimental results show that tire can be conveniently and accurately inflated or deflated to the preset pressure value in this way under the different condition, accuracy can reach ±0.04bar. It is suitable for various automatic inflators, and it is convenient, highly effective and practical.

Key words: tire pressure, preset value, micro-controller, fuzzy control, genetic approximation

中图分类号:

潘小波. 基于模糊与遗传逼近策略的轮胎预设定值充放气控制[J]. 计算机应用, 2013, 33(11): 3305-3308.

PAN Xiaobo. Tire preset value inflation or deflation control based on fuzzy and genetic approximation strategy[J]. Journal of Computer Applications, 2013, 33(11): 3305-3308.

[1]	梁臻, 房体育, 李金屏. 基于虚拟导航线的农业机器人精确视觉导航方法[J]. 计算机应用, 2021, 41(1): 191-198.
[2]	胡润彦, 李翠然. 基于模糊控制的自供能无线传感器网络分簇算法[J]. 计算机应用, 2020, 40(9): 2691-2697.
[3]	林政, 吕霞付. 基于改进模糊算法的水面无人艇自主避障[J]. 计算机应用, 2019, 39(9): 2523-2528.
[4]	孙功武, 谢基榕, 王俊轩. 基于动态遗忘因子递推最小二乘算法的船舶航向模型辨识[J]. 计算机应用, 2018, 38(3): 900-904.
[5]	胡黄水, 沈玮娜, 王出航, 张邦成. 基于自适应模糊控制器的无线传感器网络功率控制[J]. 计算机应用, 2017, 37(9): 2470-2473.
[6]	甘峰浩, 牛玉刚, 贾廷纲. 基于模糊压缩感知的无线传感网络拥塞控制算法[J]. 计算机应用, 2015, 35(9): 2430-2435.
[7]	彭玉青, 李木, 张媛媛. 基于改进模糊算法的移动机器人避障[J]. 计算机应用, 2015, 35(8): 2256-2260.
[8]	张小亮朱青王耀南曹时伟. 基于软启动技术的车身控制器设计与应用[J]. 计算机应用, 2013, 33(04): 1187-1190.
[9]	邓霏陈建政李文宝. 便携式列车广义舒适度检测系统设计[J]. 计算机应用, 2012, 32(11): 3225-3231.
[10]	李跃宗王鹏玲林轩王青元. 高速列车自动驾驶优化算法[J]. 计算机应用, 2012, 32(11): 3221-3224.
[11]	王佳. 模糊控制在垂直切换判决算法中的应用[J]. 计算机应用, 2012, 32(10): 2704-2706.
[12]	张志杰. 基于测距特征向量匹配和模糊控制的室内清洁机器人路径规划[J]. 计算机应用, 2012, 32(09): 2527-2529.
[13]	王海珍廉佐政滕艳平. 智能办公环境温度控制方法[J]. 计算机应用, 2012, 32(09): 2545-2547.
[14]	葛宇王学平梁静. 改进的混合蛙跳算法[J]. 计算机应用, 2012, 32(01): 234-237.
[15]	蔡植善. 基于通用分组无线业务短信控制的LED点阵屏设计[J]. 计算机应用, 2011, 31(12): 3449-3452.