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Reduced-Order Modelling of LTI Systems by Using Routh Approximation and Factor Division Methods

机译:使用劳斯逼近和因子划分方法对LTI系统进行降阶建模

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摘要

In this paper, a new model reduction technique for the large-scale continuous time systems is proposed. The proposed technique is a mixed method of Routh approximation and factor division techniques. In this technique, the Routh approximation method is applied for determining the denominator coefficients of the reduced model and the numerator coefficients are calculated by the factor division method. The proposed technique has two main advantages as it gives the stable reduced-order model if the original model is stable and ensures the retention of first r number of time moments of the actual system in the rth-order reduced system. This method is also applicable for those systems for which Routh approximation method fails. To illustrate the proposed method, a real-time system model is reduced where the reduced model retains the fundamental properties of the actual model. In order to examine the efficiency, accuracy and comparison to other existing standard model reduction methods, the presented technique has been verified on two standard numerical examples taken from the literature.
机译:本文提出了一种新的大规模连续时间系统模型约简技术。所提出的技术是劳斯逼近法和因子除法技术的混合方法。在该技术中,采用劳斯近似法确定简化模型的分母系数,并通过因子除法计算分子系数。所提出的技术具有两个主要优点,因为如果原始模型是稳定的,它可以提供稳定的降阶模型,并确保在r阶降阶系统中保留实际系统的前r个时间矩。此方法也适用于Routh逼近方法失败的系统。为了说明所提出的方法,在减少的模型保留了实际模型的基本属性的情况下,对实时系统模型进行了简化。为了检查效率,准确性和与其他现有标准模型简化方法的比较,在从文献中选取的两个标准数值示例中对提出的技术进行了验证。

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