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Online identification of continuous-time systems with multiple-input time delays from sampled data using sequential nonlinear least square method from sampled data

机译:使用序列非线性最小二乘法从采样数据在线识别具有多个输入时延的连续时间系统

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This research considers the problem of online identification of continuous-time system with multiple unknown time delays from sampled data. The presented algorithm estimates, simultaneously, the linear parameters and the multiple time delays. Therefore, a Sequential Nonlinear Least Square algorithm is used. Indeed, we propose a new formulation of the identification problem which permits the definition of the time delays and the parameters in the same estimated vector. Numerical example is presented to illustrate the robustness of the proposed algorithm.
机译:本研究考虑了从采样数据中获得多个未知时延的连续时间系统的在线辨识问题。提出的算法同时估计线性参数和多个时间延迟。因此,使用了顺序非线性最小二乘算法。实际上,我们提出了一种识别问题的新表述,它允许在相同的估计矢量中定义时间延迟和参数。数值例子说明了所提算法的鲁棒性。

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