...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Industrial Electronics >Data-Driven Modeling and UFIR-Based Outlet NO_x Estimation for Diesel-Engine SCR Systems
【24h】

Data-Driven Modeling and UFIR-Based Outlet NO_x Estimation for Diesel-Engine SCR Systems

机译：柴油机SCR系统的数据驱动建模和基于UFIR的出口NO_X估计

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

An accurate and efficient model for selective catalytic reduction (SCR) systems plays an important role in diagnosis and control of diesel-engine after-treatment systems. In this paper, we investigate the data-driven modeling of SCR systems and outlet NOx concentration estimation based on the developed data-driven model and the algorithm of unbiased finite impulse response (UFIR) filtering. The structure used for the data-driven model is an autoregressive exogenous (ARX) model and the method of partial least square is utilized to identify the parameters of the corresponding ARX model. Moreover, the approach of fuzzy c-means is employed to partition the data and derive multiple local linear models with a better performance on approximating the system nonlinearities. Finally, the algorithm of UFIR filtering is adopted to estimate the outlet NOx concentration due to its strong robustness without the statistics of process and measurement noises. The performance of proposed approaches on SCR systems is validated with simulations based on experimental data. In addition, comparisons show the improvement of the adopted algorithm on the estimation accuracy.
机译：用于选择性催化还原（SCR）系统的准确和有效的模型在柴油发动机后处理系统的诊断和控制中起重要作用。在本文中，我们研究了SCR系统和出口NOx浓度估计的数据驱动建模，基于开发的数据驱动模型和非偏见有限脉冲响应（UFIR）滤波的算法。用于数据驱动模型的结构是自回归的外源性（ARX）模型，并且利用部分最小二乘法来识别相应的ARX模型的参数。此外，采用模糊C型方法的方法来分区数据并导出多个本地线性模型，在近似系统非线性上具有更好的性能。最后，采用UFIR滤波的算法来估计由于其强大的鲁棒性而没有过程和测量噪声的统计。基于实验数据的仿真验证了SCR系统上提出的方法的性能。此外，比较显示了采用算法对估计准确度的改进。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

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