运输汽车衡称重量精度优化仿真
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摘要
对运输汽车的衡称重采样点确定过程的优化,能够有效提高汽车的衡称重的精度。对汽车衡称重采样点的确定,需要筛除衡称重过程的粗大误差,利用平方根矩阵对称重采样点进行优化,完成衡称重精度的优化。传统方法先推导出称重测量系统传递函数关系,并进行了离散化处理,但忽略了对称重采样点进行优化,导致精度优化效果不理想。提出基于采样点自适应确定优化的运输汽车衡称重方法,首先计算运输汽车衡称重时的位置误差,并利用最小二乘法进行拟合求解;然后计算运输汽车的定位误差相似度和标准差,去除冗余信号;最后利用3e准则筛除运输汽车衡称重的粗大误差并进行求和,取平均值作为经过称台的运输汽车轴重测量值;在此基础上,利用平方根矩阵对运输汽车衡称重的基础采样点进行优化。仿真结果证明,所提方法能够提高运输汽车衡称重的精度,对车辆运输的现代化管理具有重要意义。
This article proposes a method for automobile weighing apparatus weigh based on sampling point adaptive determination optimization. Firstly,the position error during automobile weighing apparatus weigh is calculated,and least square method is used to carry out fitting solution,then similarity and standard deviation of the position error of transport car is calculated,and redundant signal is eliminated. The gross error of the weighing is eliminated by using 3ecriterion,and then carried out the summation. The mean value is regarded as the measured value of automobile axis-weight. The basic sampling point of the automobile weighing apparatus weigh is optimized by the square-rooting matrix. The simulation results show that the method can improve the weighing precision and has significance for modernization management of vehicle transport.
This article proposes a method for automobile weighing apparatus weigh based on sampling point adaptive determination optimization. Firstly,the position error during automobile weighing apparatus weigh is calculated,and least square method is used to carry out fitting solution,then similarity and standard deviation of the position error of transport car is calculated,and redundant signal is eliminated. The gross error of the weighing is eliminated by using 3ecriterion,and then carried out the summation. The mean value is regarded as the measured value of automobile axis-weight. The basic sampling point of the automobile weighing apparatus weigh is optimized by the square-rooting matrix. The simulation results show that the method can improve the weighing precision and has significance for modernization management of vehicle transport.
引文
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[2]王中立,李丽宏.基于石英传感器的动态称重数据处理算法研究[J].传感技术学报,2017,30(2):236-241.
[3]王宁波.桥梁动态称重技术适用范围与测试精度研究[J].桥梁建设,2015,45(5):36-41.
[4]黄如林,等.基于激光雷达的无人驾驶汽车动态障碍物检测、跟踪与识别方法[J].机器人,2016,38(4):437-443.
[5]王红军,等.二维横向剪切干涉中采样点的选取[J].光子学报,2015,44(3):312002-0312002.
[6]吴青坡,丛源材,周绍磊.非线性确定采样型滤波器采样点优化算法[J].弹箭与制导学报,2016,36(5):137-141.
[7]张晨皓,等.基于选择采样的高效迭代相位提取算法[J].应用光学,2016,37(1):64-68.
[8]李惠敏,李晓林.关于车载动态称重算法研究[J].计算机仿真,2016,33(11):140-143.
[9]谭爽,李丽宏.基于灰色神经网络的汽车动态称重数据处理[J].传感技术学报,2016,29(8):1205-1209.
[10]黄必飞,等.压电薄膜车辆动态称重系统算法研究[J].传感技术学报,2016,29(6):941-946.