PID controllers are among the most commonly used control algorithms in industry. You’ll find them in process control, power generation, HVAC, CNC machines, production lines and drive systems. At first glance, the subject seems straightforward. Three parameters, a few formulas, and you’re done. In practice, however, it is precisely with PIDs that the most operational problems, instability and unnecessary downtime arise.
In this article, I look at PID controllers in practice from an engineering perspective, rather than a textbook one. I focus on how PID behaves in a real-world system, how to tune it to cope with disturbances rather than just following a set of slides, and what details determine whether the loop runs stably for months or requires constant intervention.
In practice, PID controllers are more than just three settings
In theory, a PID controller consists of proportional, integral and derivative components. In practice, however, almost no industrial controller operates in such a ‘pure’ form. Controller manufacturers add filters, limitations, operating modes and protective mechanisms, which in reality determine the quality of the control.
A D-term without filtering amplifies measurement noise. An I-term without safeguards leads to an increase in the integral and a loss of control over the output. Conversely, a P-term set too aggressively causes oscillations, which operators attempt to dampen manually. This is why PID controllers in practice always incorporate additional mechanisms, even if the user is unaware of this.
A well-designed PID loop is not meant to be ‘fast on the graph’. It is meant to be predictable, resistant to disturbances, and safe for the process and the actuators.
What is more important in industry than responding to a surge in demand?
In many training materials, PID controllers are tuned to respond to changes in the setpoint. In industry, however, this is often of secondary importance. Far more often, the controller has to cope with load disturbances. These include changes in the flow rate of the medium, ambient temperature, material viscosity, tool wear or power supply conditions.
In practice, a well-tuned PID controller should dampen disturbances without causing erratic output movements. A control loop that responds calmly and steadily usually outperforms one that ‘looks good’ when the setpoint changes abruptly, but then reacts to every single impulse.
That is why, in industrial applications, it is the balance between control speed and control smoothness that matters. Excessive aggressiveness always comes back to haunt you in the form of operational problems.
The object model as the foundation for effective tuning
Tuning a PID controller without any object model is like driving a car without a speedometer. It’s possible, but it’s hard to call it control. In practice, a vast number of systems can be described with a good degree of accuracy using a first-order model with delay.
This simple model allows us to estimate the process gain, time constant and delay. The delay proves to be particularly significant. The greater its contribution to the system’s dynamics, the higher the risk of oscillation and the more carefully the controller gain must be selected. Many unstable PID loops are not the result of a flawed theory, but of ignoring the system’s delay. The controller reacts before it can see the effect of its action, and by that time the integral term has had time to ‘build up’.
Traditional tuning methods and their limitations
Ziegler–Nichols is one of the best-known methods for PID tuning. It provides a quick starting point, but often results in aggressive settings and significant overshoot. In modern industrial installations, these values are rarely finalised.
The Cohen–Coon method performs better with objects exhibiting a distinct delay, but it also requires adjustments in a real-world scenario. You should regard both methods as diagnostic tools rather than ready-made solutions.
In practice, PID controllers almost always require manual adjustment of the settings after initial commissioning. An engineer’s experience and observation of the loop behaviour are more valuable than blindly sticking to formulas.
IMC tuning and the Lambda approach as an industry standard
In many industries, IMC tuning—also known as the Lambda approach—has become very popular. Rather than maximising response speed, the engineer selects a target level of ‘smoothness’ for the control loop and tunes the controller to achieve this.
This approach results in smoother control, greater resilience to changes in system parameters, and reduced stress on the actuators. In practice, it is precisely these types of control loops that operate for years without the need for constant adjustments. If a PID controller is to operate stably in an industrial environment, IMC very often proves to be the best starting point.
Anti-windup as a prerequisite for stable operation
Integral gain is one of the most common issues in PID loops. When the controller output reaches its limit but the integral term continues to increase, overshoot and a long settling time occur once the system emerges from saturation.
That is why anti-windup mechanisms are now standard practice. They limit the accumulation of the integral term or adjust it based on the actual output. Without this, even well-chosen settings become meaningless. In practice, every PID loop controlling a valve, inverter or servo should have active anti-windup protection.
Setpoint weighting and derivative filtering
Changing the setpoint often causes an undesirable jolt in the output. This is because the P and D terms respond directly to the setpoint error. The solution is to apply setpoint weighting, which allows the controller’s response to disturbances to be separated from its response to a change in the setpoint.
Derivative filtering plays an equally important role. Without it, the controller responds to measurement noise rather than to actual changes in the process. In practice, the D term is often reduced or switched off entirely if it does not add any real value. In practice, PID controllers rarely use the full PID in its textbook form.
Smooth switching between MAN and AUTO modes
In a real-world plant, operators switch the control loops between manual and automatic modes. If the controller does not support a smooth transition, a step change in the output occurs, posing a risk of process damage.
That is why mechanisms are used to track and synchronise the integral value with the current output. A well-designed controller switches to AUTO mode without any noticeable effect on the system. This is a detail that is rarely mentioned in theory, but in practice it is crucial to operational safety.
Car tuning as a support tool
Autotuning can quickly determine sensible initial settings. It works particularly well when the process is reasonably linear and you can afford to run tests. However, it does not replace an understanding of the process. Autotuning does not know the cost of over-adjustment, does not take mechanical wear into account, and does not understand production priorities. Treat it as a starting point, not a final solution.
How should a PID controller be evaluated in practice?
A good PID loop isn’t always the fastest. Often, the best one is the one that operates smoothly, doesn’t wear out the actuators and effectively suppresses disturbances. In addition to settling time, it is worth monitoring the level of oscillation, output stability and how the loop behaves when conditions change. It is these characteristics that determine the quality of control in the long term.
Summary
PID controllers remain the cornerstone of industrial automation, but only if they are used with care. Tuning them ‘by eye’ and copying settings from other systems leads to problems that are difficult to resolve later on.
In practice, PID controllers require an understanding of the system, an awareness of its limitations, and a conscious trade-off between speed and stability. Where engineers think in terms of the process, PID works reliably. Where it is treated as a magical black box, problems soon arise.
