Yet they add delay in sensing the true state of a process, and this has negative consequences in that as delay increases, the best achievable control performance decreases. Signal filters offer benefit in process control applications because they can temper the large CO moves caused by derivative action of noisy PV measurements. More specifically, the filtered signal has an increased dead time and time constant relative to the behavior of the actual process. The filtered signal in the plot above, though perhaps visually appealing, clearly lags behind the actual dynamic response of the unfiltered signal. In these articles we offer only an introduction to the basic methods and ideas. Filters can collect and process data at a rate faster than the control loop sample time, so many data points can go into a single PV sample forwarded to the controller.Ĭlearly, filter design is a substantial topic. A “better” filter design is one that decreases the random variation while retaining more of the true dynamic information of the original signal.įilters can be analog (hardware) or digital (software) high, low or band pass linear or nonlinear designed in the time, Z-transform or frequency domain and much more. The plot below shows the random behavior of a raw (unfiltered) PV signal and the smoother trace of a filtered PV signal.Īs the above plot illustrates, a filter is able to receive a noisy signal and yield a signal with reduced random variation. Rather than “fix” the noise by hiding it with additional or modified algorithms, we should attempt to reduce or eliminate the problem through normal engineering and maintenance practices. It is important to emphasize that before we try to filter away a problem, we should first work to understand the source of the random error. The mixing patterns can cause lower-frequency random variations in the temperature signal that are unrelated to changes in the bulk vessel temperature. This category borders on the philosophical as to what constitutes a disturbance to be controlled versus noise to be filtered.īubbles and splashing that randomly corrupts liquid pressure drop measurements is an example of process noise that might benefit from filtering.Ī less clear candidate for filtering is a temperature measurement in a poorly-mixed vessel. Process noise tends to be lower in frequency. too-large measurement span relative to operating range). ▪ quantizing of signal samples into overly-broad discrete “buckets” from low resolution or improperly specified instrumentation (e.g. ▪ jitter (clock related irregularities such as variations in sample spacing) Signal noise tends to have higher frequency relative to the characteristic dynamics of process control applications (i.e., processes with streams comprised of liquids, gases, powders, slurries and melts). Random behavior in the PV measurement arises because of signal noise and process noise. This extreme control action will increase the wear on a mechanical FCE (e.g., a valve) and lead to increased maintenance needs. In our study of the derivative mode of a PID controller, we explored how noise or random error in the measured process variable (PV) can degrade controller performance.Īs discussed in that article, derivative action can cause the noise in the PV measurement to be reflected and amplified in the controller output (CO) signal, producing “chatter” in the final control element (FCE).
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