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

机译:LMS自适应方法在时间延迟估计中的应用估算

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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高阶系统,长时间延迟。这些特征将在构建模型中带来很多麻烦。有时我们需要知道在设计控制器时系统的纯延迟,并同时我们希望模型有低顺序。传统方法等单位步骤响应和估计时间延迟的梯度近似值具有一些限制。在本文中,我们将使用LMS(最小均值)自适应方法来估计时间延迟,然后使用ARMAX模型来减少订单。已经使用实际工业数据进行了模拟。工业系统的订单甚至可以达到30次,虽然我们想要使用这种新方法将其命令减少到约10.最后,通过比较3个模型的性能指数,我们证明了这种方法可以达到所需的目的。

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