“PID auto-tuning” or “PID self-tuning” controllers are designed to simplify matters by choosing their own PID tuning parameters based on some sort of automated analysis of the controlled process’s behavior.
Theoretically, the most basic PID auto-tuners simply automate the manual PID tuning procedures: force a change in the controller effort (bump or step tests), observe the results, and adjust the tuning parameters accordingly. Practically, however, bumping a process just for the purpose of tuning the PID controller can be impractical in applications where fluctuations in the process variable must be minimized at all times. Moreover, while conceptually simple, step tests can be a challenge to automate. The results will be skewed if a disturbance happens to intrude on the process variable while the test is in progress. That problem is particularly acute when the process variable is also subject to measurement noise since it can corrupt the calculation of the process model by obscuring the exact shape of the reaction curve.
Some PID auto-tuners use heuristic methods; skip the process modeling (system identification) operations. Instead, they rely on measurements of the process’s open-loop or closed-loop performance, such as the period of oscillation, overshoot, and damping of the disturbance or setpoint response. The controller’s PID parameters are then derived from those performance measurements by means of tuning rules that mimic an experienced operator’s heuristic PID tuning technique. But, there are drawbacks to automated heuristic in the tuning as well. If the patterns of process behavior the PID auto-tuner is trained to recognize don’t occur, or if the process behaves in an entirely unexpected manner, the auto-tuner won’t know what to do. Moreover, heuristic tuning also can take a long time and several iterations to reach a final result and auto-tuners tend to be conservative about how much and how often they tweak their tuning parameters lest they should end up overdoing it.
Perhaps the most rigorous approach to auto-tune PID controller, and certainly the most complex, is numerical curve fitting: computing the parameters of a process model that best fits the available input/output data (system identification). The appropriate PID tuning parameters for the PID controller can be derived from the process model.
But, even with such enhancements, auto-tuners are generally not very accurate. Operators often take an auto-tuner’s results as recommendations that require further refinement by traditional trial-and-error PID tuning techniques.
PID auto-tuning is “still no panacea,” and as they rightly suspect, “Perhaps the most significant challenge is an unpredictable or nonlinear process.” This turns out to be true for most processes, which is why auto-tuning has achieved limited success despite a number of industry attempts. This explains why single-loop PID tuning and multivariable control modeling (system identification), which in theory should be one-time engineering tasks, are more like recurring maintenance in practice.
Two common solutions, unfortunately, promise not to solve these problems. One is the idea of an average process model or average tuning parameters. The second idea is auto-tuning or adaptive modeling (system identification), which are potentially more problematic than average because they basically tune for today, which may or may not be appropriate tomorrow.
Another challenge that has vexed PID loop tuning and model-based control is traditional error-minimization performance criteria, often inappropriate even where the related process models are reliable. In industrial process operations, it is usually more important to observe process speed limits, carefully preserve process stability, and avoid overshoot and oscillation. Aggressive minimization of interim error is usually neither a large earner nor desirable behavior in industrial process operation.
— Author: Jack Smith, Control Engineering
However, loop tuning is more of an art than a science in practice. The best choice of tuning parameters depends on a variety of factors including the dynamic behavior of the controlled process, the performance objectives specified by the operator, and the operator’s understanding of how tuning works. A variety of manual techniques have been developed to help operators tune their loops, but even with the aid of loop-tuning software, loop tuning can be a difficult and time-consuming chore.
PiControl Solutions has developed offline and online version of PITOPS software for closed-loop system identification, PID tuning parameters optimization and also for the design of APC (Advanced Process Control) schemes by way of allowing the user to design and calculate parameters for control schemes. PITOPS has several unique distinguishing functions that are modern and unmatched in competitor methods and tools: These are:
- – Works with completely closed-loop data without any step tests with the PID loops in auto or even cascade modes. Steps are not needed, ramping of the setpoint or any complex trajectory of moves in the setpoint can be used. Open-loop transfer functions can be identified even with completely closed-loop data.
- – Can process multivariable inputs, so several open-loop transfer functions can be identified simultaneously with closed-loop data.
- – In addition to the capability of identifying multivariable transfer functions simultaneously, PITOPS can tune any number of master-slave chains. There is no limit on the number of slave-cascade PIDs.
- – Works entirely in the time domain, so the user is working with time – milli-seconds, second or minutes. Users do not need advanced process control education or a lot of experience.
- – Works amazingly well amidst severe noise and disturbances. PITOPS can even isolate the unmeasured noise and disturbances and identify the true transfer function.
- – Identified the dead time automatically. Do not have to specify a fixed dead time like in many other software tools.
- – The PID tuning optimizer allows configuring a custom simulation based on typical setpoint changes, noise and disturbances and then optimizes for low error between the PV and SP while respecting the rate of change of the slave setpoint or controls valves. This functionality produces PID tuning parameters superior to conventional methods like Lambda, Ziegler Nichols, Cohen Coon, and IMC.