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Identification of continuous systems via markov parameters and time moments

机译:通过马尔可夫参数和时间矩识别连续系统

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摘要

This paper proposes an identification algorithm for multi-input and multi-output (MIMO) continuous systems via the Markov parameters and the time moments. The original system to be identified is transformed into a continuous-time system such that the transformed system is stable, the generalized time moments M{sub}i(α) of the transformed system exist even for unstable original systems, and the Markov parameters G{sub}i of the original system are equal to M{sub}i(α) /i!(i=0, 1, …). The minimum realization of the MIMO continuous-time system is identified by the ε-minimal realization method using the Markov parameters, which can be achieved by approximately calculating the generalized time moments of the transformed system using the input-output data sampled from the impulse response of the original system. Several illustrative examples will show that the algorithm provides good identification results even if the original system is unstable and there exists observing noise.
机译:本文通过Markov参数和时间矩来提出了一种用于多输入和多输出(MIMO)连续系统的识别算法。 要识别的原始系统被转换为连续时间系统,使得变换系统是稳定的,变换系统的广义时间矩M {Sub} I(α)即使是不稳定的原始系统也存在,以及Markov参数G 原始系统的{sub} I等于M {sub} i(α)/ i!(i = 0,1,...)。 MIMO连续时间系统的最小实现由使用Markov参数的ε-最小实现方法识别,这可以通过从脉冲响应中采样的输入输出数据大致计算变换系统的广义时间矩来实现 原始系统。 几个说明性示例将显示该算法即使原始系统不稳定,并且存在观察噪声,也可以提供良好的识别结果。

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