Beyond PID: 6 advanced strategies to add value to modern process control

While PID control is a good choice for most process control applications, sometimes the application is too challenging or complicated. When that’s the case, six strategies can simplify the process.

ByWilliam Hughes, PE August 28, 2023

Courtesy: Maverick Technologies

Learning Objectives

Understand proportional-integral-derivative’s (PID’s) role in process control applications.
Learn about six strategies that can help complement PID in challenging situations.

PID insights

Proportional-integral-derivative (PID) is a valuable tool for process control applications, but sometimes the application is too complex for basic PID.
Strategies such as PID tuning, signal filtering, cascade control, feedforward control and gain scheduling can help solve some of these challenges.
莫del predictive control (MPC) helps design a mathematical model that resides in the controller.

The proportional-integral-derivative (PID) control scheme is a popular approach used in programmable logic controllers (PLCs) and distributed control systems (DCSs) to control complex processes and manage dynamic industrial systems. Many available online resources center around the fundamentals of setup and control loop tuning methods used to control errors and minimize process fluctuations – including how to pick the initial gains, when to use derivative control, how to adjust a PID for response time, how to reduce overshoot and more.

While PID control is a good choice for most industrial control applications, it’s sometimes not enough to handle a system that is challenging or can’t be boiled down to a simple setpoint (SP), process variable (PV) and control variable (CV). Some systems have large deadtimes, which means the time it takes for a change in the CV to make a noticeable change in the PV. PID is most effective on processes that are linear in their control range, so processes with non-linear responses, such as pH, can be difficult to control with a PID loop.

1. Tune the instruments for better PID results

Well-tuned instruments improve PID:光伏输入PID驱动器偶然发生的一切pens in the rest of the control loop. A noisy measurement will make the error calculation (the difference between the SP and PV) erratic. If derivative control is presented with a significant change or fluctuation in the error within a single cycle of input and output (I/O) scans, then the derivative is gigantic, and the CV will be slammed to a minimum or maximum value immediately. Adjusting or replacing the instrument producing a noisy PV can turn an erratic control loop into a well-behaved system.

2. Signal filtering cleans PID information

Ensure PID uses clean data:Some process properties are noisy, and the instrumentation accurately records this noise. One way to deal with this noise is signal filtering. This approach involves applying a time-based filter to the PV and replacing the signal’s instantaneous value with a value averaged over a certain time window.

When choosing a filtering scheme, the goal should be to preserve as much meaningful signal as possible while eliminating noise. It’s important to recall that the integral term of the PID is a time-filtered evaluation of the error, so the filtering is essentially applied twice. Some control loops are tuned too aggressively for the noisy PV they’re trying to control. In this instance, reducing the gains to de-tune the PID would work better than signal filtering.

Signal filtering can hide important process information from operators and control loops, so use it sparingly and intentionally.

3. Cascade control, using one PID to control another

Controlling a challenging PV:Another way to control a challenging PV is to use one PID to control another PID, which is known as cascaded PID control. The high priority PV is fed to a slow-moving primary PID, which sends a dynamic SP to a fast-moving secondary PID that controls a different, secondary PV, which then influences the primary PV via process interactions. For example, a steam heat exchanger’s pressure is a fast-moving property that can be adjusted to control the slow-moving temperature of the fluid exiting the exchanger. Controlling the secondary PV – pressure – gets the primary PV – temperature – to its SP faster and keeps it there with little or none of the oscillation that would be present using a single PID to control temperature directly.

Basic proportional-integral-derivative (PID) control is often sufficient for process control applications, but there are other tools available there to augment its capabilities. Courtesy: Maverick Technologies

4. Feedforward control: Change based on an anticipated future

Change the future before it gets here:Control system designers sometimes know precisely how a PV will react to a change in CV, and they can encode that knowledge in the control strategy. This is broadly known as feedforward. A PID system is fundamentally a feedback scheme, in which the CV is changed, and the resulting PV change is measured to calculate a new error term. In feedforward, the CV change is used in the control logic to calculate an expected change in the error term, and this is used in the PID logic. Many modern PID control logic objects include an input for this feedforward term, which usually has its own gain tuning constant. Often if a Proportional-Integral (PI) controller is struggling, the best way to improve it is to add some feedforward instead of adding derivative control, if a suitable leading feedforward variable can be identified.

5. Gain scheduling sometimes can help PID

How gain scheduling can help PID:For processes that are nonlinear over the intended operating range, PID control can still be an option if each segment of the operating range has an essentially linear response. The proportional, integral, and derivative gains can be determined for each segment and then programmed to be changed in the PID object as the process moves along different linear domains. This is often known as gain scheduling. Exercise caution with gain scheduling and pay special attention to the boundaries between gain regimes. If the process is often operated close to a boundary, consider changing the gain schedule to avoid constant switching between gains.

6. Model-predictive control takes advantage of controller power

莫del-predictive control uses modern controllers to help processes:Faster central processing units (CPUs) are included in modern PLCs and DCSs. Some may recall those long-gone days of assiduously managing controller memory usage and clock cycles to avoid controller faults or skipped routines. Today’s controllers feature CPUs with amazing calculation capability, which has unlocked the control-scheme innovation called model-predictive control (MPC). In MPC, control system engineers collaborate with experts on the physical process to design a mathematical model of it that resides in the controller. This real-time process model enables the control logic to predict all relevant PV changes for different CV settings throughout the whole process. These predictions are used to identify the best CV changes required to achieve the desired overall process state.

Don’t abandon hope for an automated, closed-loop solution

For controlling a complex process or dynamic industrial system, basic PID control is often sufficient, but it is by no means the only tool available. There are many different options available to control these processes and systems. Combine these options in various ways to achieve the outcomes that operations personnel and production demands require. When a process seems doomed to oscillation and requires constant, active supervision by operations personnel, consider the advanced control strategies reviewed here. They can turn the most challenging process into a reliable, high-uptime, almost invisible part of a smooth-running facility.

William Hughes, PE(william.hughes@mavtechglobal.com) is a senior engineer atMaverick Technologies, a CFE Media and Technology content partner. Edited by Chris Vavra, web content manager,Control Engineering《媒体和技术,cvavra@cfemedia.com.