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Low-pass filtering means that low-frequency signals can pass through, but high-frequency signals will be filtered out. It is mainly used to remove signal burrs and interference. It is widely used in engineering.
The basic theoretical formula of low-pass filter is:
y(t) = K*u(t) + (1-K)*y(t-1) = y(t-1) + K*[u(t)-y(t-1)]
Among them, K=dT/T, K is generally between 0 and 1, dT is the running step length, T is the time constant; u is the input signal; y is the output signal.
Generally, for a certain controller, its operating cycle is fixed, so the time constant can only be adjusted to change the filtering effect.
Based on the above formula, we first build a basic low-pass filter simulink model, as shown below:
It can be seen from the figure that the output of the filter is the output at the previous time, plus a scale factor multiplied by the current input minus the output at the previous time. Just imagine, if the scale factor is 1 (that is, T=dT), the output at the current moment is equal to the input; if the scale factor is between 0 and 1, the output at the current moment must be slower than the input, which will cause hysteresis. The advantage is that the input will be attenuated by this scale factor when there is a sudden large change. Therefore, the advantage of the low-pass filter is that it can filter out large high-frequency fluctuations, but the disadvantage is that it will be delayed compared to the original signal.
It can be seen that the signal becomes smoother after filtering, and as the time constant T increases, the curve delay after filtering becomes larger. Therefore, in actual use, the selection of the time constant T is particularly important. We need to comprehensively consider the control system's requirements for signal accuracy and signal delay, and choose a time constant that meets our requirements.
Simulink also has its own analog filter, as shown in the figure below.
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