PID Control May Struggle With Noise But There are Numerous Applications Where It’s the Perfect Fit.
A previous post about the Derivative Term focused on its weaknesses. As noted, the primary challenge associated with the use of Derivative and PID Control is the volatility of the controller’s response when in the presence of noise. Noise is a major stumbling block for Derivative and PID Control as production data is routinely replete with process noise and other sources of variability. The use of PID Control in such an environment can drive frenetic changes in a loop’s Controller Output (CO) and unnecessarily wear out the associated Final Control Element (FCE). In summary: Little to gain; lots to lose.
Derivative’s difficulty with noise notwithstanding there are plenty of industrial applications for which PID Control provides significant value. They’re often nonlinear processes, ones that exhibit a high degree of inertia, or processes that allow you to either add or subtract to the Process Variable…but not both. A common attribute of such processes is that they respond slowly to changes. The data doesn’t shift direction in seemingly nonstop fashion. As a result, the Process Variable is not constantly changing direction and the slope of any changes are generally modest.
Consider the following applications for which Derivative and PID Control are well suited:
- Furnace Temperature Control
Furnaces typically involve heating and holding large amounts of raw material at high temperature. It’s commonplace for the material involved to have a large mass. As a result it possesses a high degree of inertia – the material’s temperature doesn’t change quickly even when high heat it applied. This characteristic results in a relatively steady PV signal, and it allows the Derivative term to effectively correct for Error without excessive changes to either the CO or the FCE.
- Neutralization pH Control
pH is widely viewed in industry as a challenge to control. For one: pH is highly non-linear – its behavior changes from one operating range to another. For another: The buffering effects of some material can curb what would otherwise be volatile dynamics until the buffer is saturated. While the dynamics of pH are challenging from a control perspective, they are well suited for the PID form of the controller. Specifically, the dynamics of pH tend to be slow as the amount of caustic or acid that’s typically added to a process is relatively small when compared to the volume of existing liquid. The slower dynamics allow Derivative to improve control without overworking the FCE.
- Batch Temperature Control
In contrast with the furnace example mentioned above batch temperature control is basically operated as a closed system. While bubbling and other process noise will certainly be evident in the data, noise on the whole is less of an issue in a closed system. Another aspect of batch temperature control relates to temperature itself. While heat can be applied to either maintain or increase temperature, many batch temperature control processes do not include a cooling loop with which to counter the effects of heat. Said another way: Heat can be added, but it can’t be subtracted. The net effect are dynamics which are both slow and nonlinear, and noise within the data is limited. These characteristics make for an ideal application of PID Control.
There are numerous industrial applications where PID Control shines. While processes with fast dynamics and excessive noise undermine the efficacy of the Derivative Term, not all processes exhibit those characteristics. With those slower, less noisy processes Derivative contributes effectively to the correction of error.