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Why Tuning Rules Don’t Always Work

There are several reasons why PID controller tuning rules don’t always work as advertised. I have talked to several process control practitioners who tried them once or twice, but had no success and gave up on using them as a result. Here is a list of items to consider when using tuning rules.

ZIEGLER-NICHOLS TUNING RULES

The Ziegler-Nichols are the oldest and most popular tuning rules. They developed two methods: the Process Reaction-Curve or Open-Loop method (done with controller in manual) and the Ultimate-Cycling or Closed-Loop method (controller in automatic).

The Ziegler-Nichols open-loop tuning rules have several drawbacks:

Issue 1: It tunes the loop for quarter-amplitude-damping response, which overshoots and oscillates quite a bit.

Issue 2: It leaves the loop with very little robustness, which can lead to loop instability.

Issue 3: The rules give you very poor response if the process is dead-time dominant.

Issue 4: The rules are very sensitive to an accurate measurement of dead time, which is difficult on lag-dominant processes with short dead times.

The Ziegler-Nichols closed-loop tuning method does a little better with issues 3 and 4 above, but issues 1 and 2 remain a problem. In addition, this method is very sensitive to control valve problems like dead band or stiction. More info here: http://blog.opticontrols.com/archives/39

Issue 1 and 2 (and 4 to some degree) can be alleviated by using only half or less of the calculated controller gain.

COHEN-COON and OTHERS

The Cohen-Coon and many other open loop tuning rules do much better with issue 3, but issues 1, 2 and 4 are still problems. Again, these can be resolved by detuning the controller gain.

LAMBDA TUNING RULES

Lambda tuning rules give you a stable response with no overshoot, and leave the loop with plenty of robustness to accommodate measurement errors. So it seems like Lambda tuning rules overcome all of the four issues listed above. However, these rules result in a very slow response to disturbances on lag-dominant processes. More info here: http://blog.opticontrols.com/archives/260

CONTROLLER ALGORITHMS

If your controller algorithm and units of measure are not matched with the tuning rule you use, the results can be undesirable, or even dangerous.

Of the PID tuning rules mentioned above, the Ziegler-Nichols rules were developed for controllers with an interactive (series) controller algorithm, while Cohen-Coon and Lambda rules were developed for noninteractive (a.k.a. standard or ideal) controllers. The PI tuning rules (no derivative) will work on both interactive and non-interactive algorithms, while PID may require parameter conversion. If you detune the controller like explained above, the difference between interactive and noninteractive algorithms becomes much less important.

However, if you have a controller with a parallel algorithm, you definitely have to convert the calculated settings to work with it. More info here: http://blog.opticontrols.com/archives/124

And note that most tuning rules calculate controller gain, and not proportional band. And most calculate integral time (as in minutes or seconds), and not integral gain (as in repeats per minute or repeats per second). Make sure you convert the tuning settings to work on your controller.

Finally, the rules assume you are making the measurements of dead time and time constant in the same time-base used by your controller’s integral and derivative settings, i.e. minutes versus seconds. If you measure in seconds, but the controller uses minutes, you have to convert your measurements to minutes before calculating tuning settings.

CONTROLLER SCAN INTERVAL

Tuning rules assume controllers are analog in nature – that they continuously sample the process variable and calculate an output. However, modern controllers are digital, and execute their task intermittently at a rate called the scan interval, execution period, or something similar. Controllers typically execute at 1-second intervals, but this could be much faster or slower, depending on the application.

Intermittently scanning and executing at a 1-second interval is normally not a problem for slow loops like temperature, gas pressure, level, and composition. However for fast loops like flow, liquid pressure, and motor speed, a 1-second scan interval can add a substantial proportion of dead time to the loop. If this extra dead time is ignored in the tuning rule, controller settings will be too aggressive, possibly leading to oscillations and instability on fast loops.

OTHER PROBLEMS

Another reason why tuning rules fail to deliver on our expectation could be that the control valve or damper might be defective. This includes problems like dead band and stiction. These problems can invalidate the results you get from a step test, and affects the loop performance of even a well-tuned loop. More information here: http://blog.opticontrols.com/archives/77

WHICH RULE TO USE

You should select the tuning rule according to the desired control objective for the loop, while considering the constraints above:

– If you need a very stable loop that absorbs disturbances rather than passing them on, use the Lambda tuning rules.

– If you need fast recovery from disturbances, use the Cohen Coon tuning rules, but use only half of the rule-calculated value for controller gain to overcome issues 1 and 2. However, if the process dead-time is very short (issue 4), or the PV is noisy and you can’t measure the dead time accurately, use the Lambda tuning rules.

TUNING SOFTWARE

High-end tuning software packages can diagnose control valve problems such as dead band and stiction. They also let you specify the control objective and your controller type. They can then produce appropriate tuning settings after analyzing the process response from a step-test.

 

Stay tuned!
Jacques Smuts – Author of the book Process Control for Practitioners

 

2 Responses to “Why Tuning Rules Don’t Always Work”

  • Arkadiy Turevskiy:

    Thanks for the nice overview.
    When designing controllers (any controllers in general, including PID controllers speciically), it is a good idea to create a good plant model and to simulate your design in software before trying it on the actual process.
    Here is a page we put together with a comprehensive set of resources for tuning, simulating, and implementing PID controllers in MATLAB and Simulink:
    http://www.mathworks.com/discovery/pid-control.html

  • Arkadiy, I totally agree with you. Without a process model, you’re tuning in the dark, and simulations help you see the effect of your changes before actually implementing them in the process. While aerospace and other high-tech industries often use more complex models and controllers, for most industrial processes, a simple first order + dead time model and PI or PID control are often sufficient.

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