这里有两点值得强调：根据Impulse Factor的控制器行为和根据干扰配置的仿真性能。
首先，让我们讨论根据Impulse Factor的控制器行为。当液位（POV）的Impulse Factor使用默认值0时，结果是控制器行为变得相当好动和嘈杂。记住在SMOCPro理解中，Impulse Factor=0预示着当前预测误差取决于真实的和不会消失的扰动。因此，这种设置使控制器预测液位估计的误差随着控制时域积分。因为在每个控制器执行时，噪声贡献可能会导致CV上下移动，SMOCPro必须迅速响应任何级别的错误，并调整Outlet Flow OP（MV），这样才能取消感知到的干扰。
接下来，当我们将Impulse Factor增加到新设定值0.8，我们知道在控制器预测理解起来，这一新设定意味着只有20%的液位估计误差随着控制时域积分，而感知到80%的干扰将随着时域消失。因此，Outlet Flow OP（MV）只对其中一小部分的液位误差有反应。因此，很清楚的是控制器将达到更平滑的行为。
我们考虑的最后一个脉冲因子是Impulse Factor = 0.95。和前面一样，这里的控制器预测是仅有5％的当前液位预测误差随着控制时域积分，而95％是转瞬即逝的。Outlet Flow MV对液位噪声几乎没有反应，在液位阶跃扰动时的动作也非常平滑。
其次，让我们来测试根据干扰配置的仿真性能。通过比较仿真图后可以显而易见的是：仿真的第2组设定（干扰直接注入液位CV）与第1组设定（干扰注入UNM）相比，其噪音显著地较大。让我们通过一个例子解释这背后的原因。思考我们在本节中已经提出的例子，即注入一个标准差为1，平均值为0的噪声信号。一方面，当噪声被注入UNM时，显然值为1的噪声必须经过模型块进入液位CV。因为这个原因，它可能不是直接通过注入噪声到UNM来指定代表有意义的物理参数的噪声值。为了实现这一点必须考虑到模型的参数。

原文：
There are two items worth highlighting here: controller behavior as a function of Impulse Factor and simulation performance as a function of disturbance placement.
Firstly, let us discuss the controller behavior as a function of Impulse Factor. When the default value of zero is being used for the Impulse Factor of the Level (POV) the resulting controller behavior is quite aggressive and fairly noisy. Remember that an Impulse Factor of zero translates into SMOCPro understanding that the current prediction error is due to a real and non-vanishing disturbance. Consequently, this setting makes the controller predict that the error in Level estimation is ramping along the control horizon. Because at every controller execution the noise contribution may lead the CV to move up or down, SMOCPro must quickly react to any Level error and adjusts the Outlet Flow OP (MV) so that it cancels the perceived disturbance.
Next, as we increase the Impulse Factor to a new setting of 0.8, we understand that this new setting translates into the controller predicting that only 20% of the error in the Level estimation is ramping along the control horizon and thus 80% of the perceived disturbance will vanish along the horizon. As a consequence, the Outlet Flow OP (MV) only reacts to a small part of the Level errors. Thus, it is clear that the controller achieves smoother behavior.
The last Impulse Factor under consideration here is Impulse Factor = 0.95. As in the previous case, here the controller predicts that only 5% of the current Level prediction error is ramping along the control horizon, whereas 95% is fleeting. The Outlet Flow MV barely reacts to the Level noise and moves smoothly for the Level step disturbance.
Secondly, let us examine simulation performance as a function of disturbance placement. One thing that is plainly evident by comparing the simulation figures is that the second set (disturbance injected directly into the Level CV) of simulations is significantly “noisier” as compared to the first set (disturbance injected into the UNM). Let us explain the reason behind this with an example. Consider the case that we have presented in this section, namely injecting a noise signal with a standard deviation of 1 and zero mean. On the one hand, where the noise is injected into the UNM, a noise value of 1 has to go through the model block to manifest itself in the Level CV. For this reason, it may not be as straightforward to specify noise values that represent meaningful physical parameters by injecting the noise into the UNM. To achieve this one must take into account the model parameters.

2016.5.23