Learn what adaptive tuning is -- and is not -- as well as the factors to keep in mind when considering this technology for your application.

Adaptive tune, as the name suggests, adapts a controller’s settings to the dynamics of the process being controlled and will tune “on the fly,” responding to certain process criteria as determined by the specifics of the adaptive algorithm being used. When applied properly, adaptive tuning can be of great value in taming hard-to-tune process loops or in tuning a “typical” process loop more precisely.

Adaptive tuning is not simply autotuning. Often, when “autotune” capabilities are mentioned, what typically is meant is “predictive tune,” where the algorithm calculates the proportional, integral and derivative (PID) values for the process loop to be controlled. Once the values have been set, control of the process variable is achieved by varying only the process output percentage. As long as the process is stable, this works reasonably well. By contrast, adaptive tuning changes a control’s PID values as needed in response to changes in the process.

When considering whether adaptive tune control is right for your application, there are several questions to ask:
  • How dynamic is the process?
  • Are there overshoot problems?
  • Are tighter control and increased accuracy important?
  • Can consistent control help reduce scrap?

Processes that are operated at a wide range of setpoints such that the PID parameter values must be different at different setpoints may benefit from adaptive tuning. Likewise, processes that routinely undergo load changes such as exothermic chemical reaction or shear heat that results from plastic extrusion can benefit. For such processes, adaptive control will provide a better match of PID parameters that are optimized automatically. Adaptive control algorithms can improve tuning in nearly any process because the user no longer needs to be a tuning expert, nor is it necessary to call one in. (In fact, even a tuning expert cannot feasibly tune some processes because they require re-tuning as conditions change.)

Adaptive tune also is helpful when a tuning expert is unavailable because it applies built-in expertise. The operator simply sets up the sensor and output types, enters a setpoint, and sets the control mode to tune. Then, the algorithm takes charge. Most applications are not so dynamic that they require adaptive tuning, but almost any process can be better tuned. The resulting PID settings (proportional band, integral reset, and derivative/rate) will better reflect the thermal characteristics of the process.

The question often arises about whether adaptive tuning will over-tune an application. Most adaptive algorithms will not over-tune a loop; however, it never hurts to ask the controls provider how the particular adaptive algorithm tunes. If the provider cannot explain how it works or is vague, you may need to dig further to ensure the control provides what you are looking for. Ideally, with adaptive control, the algorithm continuously monitors the process performance and adjusts the tuning only when needed.

When a process is well tuned, processed materials are kept closer to the target setting, and that improves yield and reduces scrap and rework of mis-processed material. In addition, when the process variable tracks the setpoint better, the process spends less time warming up and stabilizing, so it is available and productive more of the time, which helps save capital and energy costs.

Most adaptive algorithms will work well across a range of processes, including both fast- and slow-responding loops. A fast-responding process often calls for a higher proportional value, a lower integral value and, in some cases, even turning the derivative to zero. By contrast, a slow-responding loop typically will call for a lower proportional value and higher integral value. Adaptive tuning automatically compensates for these differences in requirements.

The bottom line is, adaptive tuning can provide “expertise in a box.” It takes the experience of control experts and packages it in the algorithm, making it straightforward and easy for the user to implement.

Figure 1. The effect of the tune gain feature on recovery from a load change is shown.

An Example: Boiler Temperature Control

Following natural disasters such as Hurricane Katrina in 2005, trailer-mounted portable decontamination systems are used to quickly decontaminate large numbers of people helping with the cleanup. For the decontamination to be effective and minimize adverse effects on the workers, precise boiler water temperature control is critical.

Typically, the decontamination shower water temperature must be maintained at a precise temperature such as 92oF (33oC). If the water is too cool, the hazardous material might not be successfully removed. If the water is too hot, the workers could be scalded or the pores of their skin could open, increasing their exposure the very chemicals that the showers are designed to remove. To maximize decontamination capacity at work sites with a large number of workers, trailers with three or four boilers are used, but control of this number of boilers under the dynamically changing load present can be difficult.

For example, in systems with three or more boilers, unacceptable water temperature fluctuations can occur when a large number of the showers are in use, dynamically changing the hot water demands. Because the portable systems include numerous showers that can be independently turned on and off, the water flow can change quickly, and by a large volume. Despite the dynamically changing demands on the boilers, the decontamination equipment must provide precise water temperature control.

In this example, using a multiloop temperature controller with adaptive tuning minimizes unnecessary de-tuning of the boiler temperature controls and maintains the water temperature at the 92oF specification, even with different inlet water temperatures and changes in flow rates. Adaptive tuning maximizes the controller’s responsiveness to the changing water demand and adapts on the fly to the changing dynamics of the system.

Adaptively tuned controllers can help portable decontamination shower manufacturers -- and other manufacturers that require “expertise in a box” -- accomplish that level of control.

Processes that routinely undergo load changes -- exothermic chemical reaction or shear heat that results from plastic extrusion, for example -- can benefit from using controllers with adaptive tuning.

Feature-Rich Control

For applications with dynamically changing heat demands, some multiloop temperature controllers use proprietary adaptive algorithms to tune the loops automatically, minimizing setup time and effort. In addition, the adaptively tuned controllers are able to provide optimal performance by fine tuning loops more precisely than simple autotune capabilities and provide stable control through setpoint and load changes.

While there are similarities among the adaptive tune algorithms used by different temperature controller manufacturers, each manufacturer’s algorithm also has specific differences. For example, one adaptive tuning capability includes two specific features called “tune band” and “tune gain.” Tune band describes the process the control uses to decide whether to adaptively tune the PID parameter. When the variable (for example, temperature) is within a specific limit, or band, around the variable’s setpoint, the controller adaptively tunes the PID parameters. When the process variable is outside this band, tuning is not performed. The tune band feature prevents undesirable de-tuning of the PID parameters.

The second feature, tune gain, determines how responsive the adaptive tuning algorithm is to deviations from setpoint and setpoint changes (figure 1). Controller responsiveness is a user preference -- dependent upon the relative importance of preventing overshoot and minimizing time-to-setpoint. Therefore, this parameter is not set automatically, and controller responsiveness may be changed by the operator. Typically, adaptively tuned temperature controls have a range of settings, from least aggressive response and least potential overshoot (lowest gain) to the most aggressive response and most potential for overshoot (highest gain), that allow control users to selective the responsiveness level necessary for the process being controlled.

Technology in general has been advancing exponentially the last several years. Some changes are just that -- changes with limited or no real advancement. Adaptive tune functionality brings change that provide tangible benefits by improving quality, reducing defects and overall making products more user friendly.