How to prevent over-filtering in signal filtering?
Prevent over-filtering by simultaneously optimizing loop tuning and filter parameters. Click here for a version of this article with much more background on noise and explanation of filter types.
Which is an example of filtering in control?
Filtering is the modification of a measured or calculated signal—using an algorithm and/or logic—to remove undesirable aspects of the signal before it is used in a calculation or a controller. Examples in control are the feedback (or controlled) variable to a PID or APC controller, or the input to a feedforward controller.
What happens when a filter is used for feedback?
When used for feedback, the filtered value can result in control that is sluggish or, in the worst case, becomes oscillatory or even unstable. In the authors’ experience, the more typical problem encountered is excessive filtering of a signal, as opposed to under-filtering.
Why do we need to filter high frequency noise?
High-frequency noise is normally considered to be random and additive to a measured signal, and is usually uncorrelated in time; i.e., the value of the noise at any time τ does not depend on previous values of the noise. Ideally, we want to estimate the underlying signal without noise, introducing as little distortion as possible.
What are the filter parameters for a loop?
Before retuning a loop, make sure to note what the filter parameters are: time constant or filter factor and Δτ. Only use filtering to temper movement in the manipulated variable (controller output) caused by the noise.
Why is derivative action important in signal filtering?
Derivative action is important when aggressive control is required, particularly for integrating and near-integrating (lag-dominant) systems with additional lags (s) and dead time. If not properly filtered, noisy signals will cause derivative action to be ineffective due to erratic movement of the PID controller output.