The post How to Optimize PID Controller Settings and Options first appeared on the ISA Interchange blog site.
The following technical discussion is part of an occasional series showcasing the ISA Mentor Program, authored by Greg McMillan, industry consultant, author of numerous process control books, 2010 ISA Life Achievement Award recipient and retired Senior Fellow from Solutia Inc (now Eastman Chemical). Greg will be posting questions and responses from the ISA Mentor Program, with contributions from program participants.
The following discussion is based on the ISA Mentor Program webinar recordings of the three-part series on PID options and solutions. The webinar is discussed in the Control Talk blog post PID Options and Solutions – Part 1 and the post PID Options and Solutions – Parts 2 and 3. Since the following questions from one of our most knowledgeable recent protégés Adrian Taylor refer to slide numbers, please open or download the presentation slide deck ISA Mentor Program WebEx PID Options and Solutions.
Figure 1: ISA Standard Form (see slide #42 from the presentation PDF)
Figure 1 depicts the only known time domain block diagram for ISA Standard Form with eight different PID structures by setpoint weight factors and the positive feedback implementation of integral mode that enables true external-reset feedback (ERF). Many capabilities, such as deadtime compensation, directional move suppression, and the elimination oscillations from deadband, poor resolution, poor sensitivity, wireless update times, and analyzer sample times, are readily achieved by turning on ERF.
Adrian Taylor’s Question 1:
Slide 9 details a Y factor which varies between 0.28 and 0.88 for converting the faster lags to apparent dead time. You mentioned this Y factor can be looked up on charts given by Ziegler and Nichols (Z&N), are you able to provide with a copy of these charts or point me to where I can get a copy?
Greg McMillan’s Answer 1:
The chart and equations to compute Y are on page 137 of my Tuning and Control Loop Performance Fourth Edition (Momentum Press 2015). The original source is Ziegler, J. G., and Nichols, N. B., “Process Lags in Automatic Control Circuits,” ASME Transactions, 1943.
Adrian Taylor’s Question 2:
On slide 17 you give recommendations for setting of Lambda, I’m presuming these recommendations are for integrating and near integrating systems only and wondered what your recommendations are for setting of Lambda when using the self-regulating rules?
Greg McMillan’s Answer 2:
I was focused on near-integrating and integrating processes since these are the more important ones in the type of plants I worked in but the recommendations for Lambda apply to self-regulating as well. Lambda being a fraction of deadtime for extremely aggressive control is of theoretical value only to show how good PID can be if you are publishing a paper. I would never advocate a Lambda less than one deadtime unless you are absolutely confident you exactly know dynamics and that they never change and you can tolerate some oscillation. Lambda being a multiple of deadtime for robust control is of practical value for dealing with changing or unknown dynamics and providing a smooth response.
Adrian Taylor’s Question 3:
On slide 17 where the recommended Lambda settings are given, it recommends a Lambda of 3 and 6 respectively for adverse changes in loop dynamics of less than 5 and 10. What do 5 and 10 refer to?
Greg McMillan’s Answer 3:
I should have said “factor of 5” and “factor of 10” instead of just “5” and “10”, respectively in statement on robustness. These factors are actually gain margins. I also should not have rounded up to a factor of 10 and instead said a factor of 9 for Lambda 6x deadtime. While this specifically indicates what increase in self-regulating or integrating process gain as a factor of original can occur without the loop going unstable, it can be extended to give an approximate idea of how much other adverse changes in loop dynamics can be tolerated if the process gain is constant. For example, the factor applies roughly to the increase in total loop deadtime for deadtime dominant self-regulating processes and decrease in process time constant for lag dominant processes that would cause instability. This extension assumes Lambda tuning where the Lambda in every case is a factor of deadtime with the reset time being proportional to process time constant for deadtime dominant processes and reset time being proportional to deadtime for lag dominant processes. The reasoning can be seen in the equations for PID gain and reset time on slides 30 and 32 without my minimum limits on reset time.
Adrian Taylor’s Question 4:
On slide 17 there is a statement “Adverse changes are multiplicative…”. I didn’t quite understand the context of this statement if you are able to expand a little more? (Probably goes hand in hand with question 3 above).
Greg McMillan’s Answer 4:
An increase in process gain by factor of 2 will result in a combined factor of 9 for adverse changes such as a decrease in process time constant for lag dominant self-regulating processes by factor of 4.5 or an increase in loop dead time by a factor of 4.5 for deadtime dominant processes.
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Adrian Taylor’s Question 5:
On slide 31 when calculating arrest time we use a value Δ% which is described as the maximum allowable level change (%). Just to be sure I understand the value to be used here… If I had a setpoint high limit of 80% and the tank overflow is at 100% then the value of Δ% would be equal to 100-80=20%?
Greg McMillan’s Answer 5:
Yes, if the high level alarm is above the high setpoint limit. Δ% is maximum allowable deviation that is often the difference between an operating point and the point where there is an alarm.
Adrian Taylor’s Question 6:
On slide 31 when calculating arrest time we use a value Δ% which is described as the maximum allowable PID output change? Is this just simply the difference between the output high and low limits…. So if the output high limit was 100% and the output low limit was 0%, then the value of Δ% would be equal to 100-0=100%?
Greg McMillan’s Answer 6:
Yes. This term in the equation is counter intuitive but results from derivation of equation in Tuning and Control Loop Performance Fourth Edition using minimum integrating process gain.
Adrian Taylor’s Question 7:
I am going to purchase a copy of your ‘Tuning and Control Loop Performance’ book shown at the end of the presentation. I am curious if you think it is also worth purchasing the tuning rules pocket book, or if all the content of the pocket book is also contained in the larger book I am already purchasing?
Greg McMillan’s Answer 7:
The ‘Tuning and Control Loop Performance’ book is much more complete and explanatory but can be overwhelming. The pocket guide provides a more concise and focused way of knowing what to do.
Adrian Taylor’s Question 8:
At the end of webinar a question was posed around tuning loops where it is not possible to put the loops in manual, I am seeking more specifics based on my notes on procedure:
Greg McMillan’s Answer :
A closed loop procedure using your notes to give approximate tuning that keeps loop in automatic is as follows if you cannot put loop in manual or do not have software to identify the loop dynamics:
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