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Inlet NO x and NH 3 Concentration Estimation for Diesel-engine SCR Systems by Combining Data-Driven Model and Unbiased FIR Filter

机译:入口 x NH 3 通过结合数据驱动模型和无偏FIR滤波器来估算柴油机SCR系统的浓度

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Selective catalytic reduction (SCR) systems have been widely used in diesel engine applications. In an SCR system, inputNOxandNH3concentration information are of critical importance for the urea dosage controller design and system fault detection. Generally, theNOxandNH3concentration are obtained by physical sensors. However, the physical sensors do not only increase the cost of overall system, but also induce measurement delays. To deal with this issue, an input observer combining a data-driven model and an unbiased finite impulse response (FIR) filter is proposed. The structure of data-driven model is auto-regressive exogenous (ARX) model and partial least square (PLS) is utilized to identify the parameters in the ARX model. Nevertheless, fuzzy c-means (FCM) is also employed to partition the data and obtain multiple local linear models for describing the nonlinearities of the system. At last, an unbiased FIR filter is adopted to estimate the inputNOxandNH3concentration simultaneously due to its strong robustness against the noise. The comparisons between the unbiased FIR filter algorithm and Kalman filter algorithm are carried out in MATLAB/SIMULINK. The simulation results demonstrate that the performance of proposed estimator is outstanding.
机译:选择性催化还原(SCR)系统已广泛用于柴油发动机应用中。在SCR系统中,输入的NOx和NH3浓度信息对于尿素剂量控制器设计和系统故障检测至关重要。通常,NOx和NH3浓度是通过物理传感器获得的。然而,物理传感器不仅增加了整个系统的成本,而且引起测量延迟。为了解决这个问题,提出了一种将数据驱动模型和无偏有限冲激响应(FIR)滤波器相结合的输入观测器。数据驱动模型的结构是自回归外生(ARX)模型,并且使用偏最小二乘(PLS)来识别ARX模型中的参数。尽管如此,模糊c均值(FCM)也被用来划分数据并获得用于描述系统非线性的多个局部线性模型。最后,由于其强大的抗噪声能力,采用了无偏FIR滤波器来同时估算NOx和NH3的浓度。无偏FIR滤波器算法和卡尔曼滤波器算法之间的比较是在MATLAB / SIMULINK中进行的。仿真结果表明,所提出的估计器性能优异。

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