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The Application of LMS Adaptive Method in Time Delay Estimation for Order Reduction Identification

机译:LMS自适应方法在降阶辨识时延估计中的应用。

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The actual industrial systems are usually high order system with long time delay. These characteristics will bring a lot of troubles in building the models. Sometimes we need to know the pure delay of the system when designing the controller and at the same time we hope the model has low order. The traditional methods like unit step response and pade approximation to estimate time delay have some limitations. In this paper, we will use LMS(Least mean square) adaptive method to estimate time delay first and then use ARMAX model to reduced the order. The simulation has been conducted using the actual industrial data. The industrial system's order is very high even can reach 30 while we want to use this new method to reduce its order to about 10. At last, by comparing the performance index of 3 models, we prove this method can achieve the desired purpose.
机译:实际的工业系统通常是长时间延迟的高阶系统。这些特征将在建立模型时带来很多麻烦。有时在设计控制器时我们需要了解系统的纯延迟,同时我们希望模型具有低阶。传统的方法(如单位阶跃响应和pade逼近来估计时间延迟)有一些局限性。在本文中,我们将首先使用LMS(最小均方)自适应方法来估计时延,然后使用ARMAX模型来减少阶数。使用实际的工业数据进行了仿真。工业系统的阶数很高,甚至可以达到30,而我们想使用这种新方法将阶数减少到10。最后,通过比较3个模型的性能指标,我们证明了该方法可以达到预期的目的。

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