首页> 外文OA文献 >Group-SMA Algorithm Based Joint Estimation of Train Parameter and State
【2h】

Group-SMA Algorithm Based Joint Estimation of Train Parameter and State

机译:基于Group-SMA算法的列车参数和状态联合估计

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务,本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文,但由于OA文献来源多样且变更频繁,仍可能出现获取不到、文献不完整或与标题不符等情况,如果获取不到我们将提供退款服务。请知悉。

摘要

The braking rate and train arresting operation is important in the train braking performance. It is difficult to obtain the states of the train on time because of the measurement noise and a long calculation time. A type of Group Stochastic M-algorithm (GSMA) based on Rao-Blackwellization Particle Filter (RBPF) algorithm and Stochastic M-algorithm (SMA) is proposed in this paper. Compared with RBPF, GSMA based estimation precisions for the train braking rate and the control accelerations were improved by 78% and 62%, respectively. The calculation time of the GSMA was decreased by 70% compared with SMA.
机译:制动速率和列车制动操作对列车制动性能很重要。由于测量噪声和计算时间长,难以准时获得列车的状态。提出了一种基于饶-布莱克韦化粒子滤波(RBPF)算法和随机M算法(SMA)的群随机M算法(GSMA)。与RBPF相比,基于GSMA的列车制动率和控制加速度的估计精度分别提高了78%和62%。与SMA相比,GSMA的计算时间减少了70%。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号