Are the old techniques still in use, like root locus or nyquist plot

In order to pass the qualify exam, I have to revisit the old textbook
teaching how to draw a root locus and nyquist plot. Are these old
techniques still in use now?
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Some (me) still use them. Some (Peter) don't.
But Peter doesn't post here much any more, so I guess the answer is "yes".
I rarely use root locus plots, and when I do they're either really simple ones that I have memorized, or I generate them on a computer. I often do frequency-response design, for which it is convenient to generate a Bode plot and a Nyquist plot at the same time, as they each present different and valuable views of the same information.
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Tim Wescott
Hi Tim
Do you use root locus only on simple systems or do you try to simplify complex systems in such way that you can use root locus?
Best regards, pt
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In a way I still do. I use pole placement so I work the problem backwards. Instead of increasing the ONE controller gain through a range and watch where the roots/poles go I move the poles and calculate the gains. When I was learning control I found the root locus as used is useless. First who moves just one gain? A controller has 2,3 or 4 grains to set. Now how would you do the root locus moving all those gains around. All the gains must work together. Tweaking one at a time is not very efficient.
I find Nyquist plots to be equally useless for the same reason. If I had to adjust one proportional gain then OK but figuring out the curve with 3 or 4 gains is nuts.
There are better ways. I have symbolic formulas for calculating controller gains for many different types of systems. The allow me to place the poles where I want which is usually on the negative real axis in the s plane.
Sometimes I use IMC when talking to people about temperature control.
If I were teaching I would focus more on system identification and modeling mention the ancient history only briefly.
I am still in contact with one of the other guys that posted here in the past. I meet him on-line as a undergrad and now he has his master's degree and teaches. I have asked him why he teaches the same old control in the same old way. There is a lot of inertia when it comes to education. I think a lot is a waste of time.
I look from time to time but signal to noise ratio is too low if you know what I mean. There are no moderators removing spam. I get e- mails from people from time to time and that keeps me occupied. I have made a SOPDT temperature simulator that is on-line on the interenet. I can demonstrate many different forms of PID, SMC and Smithd Predictor. I recently did a webinar using gotomeeting for the forum.
In a way this group is better like this because there aren't any nuts spreading bad information.
Peter Nachtwey
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I'm guessing that the exam you mention is aimed mainly at practitioners at the process end of control, ie. big tanks and pumps. I find that dynamics as a whole is a regrettably small element of my job, and when a need to apply it arises, it can almost always be handled using intuition as much as rigorous analysis, although simulation is one of the tools that I really rely on.
A lot of my time is spent on platform matters, and non-regulatory things like MMI, reporting and alarms. Many of the applications I implement are not related to continuous loops, they include things like logic, switching and sequencing.
The vast majority of SISO, approximately linear problems in plants can usually be left to the techs, in fact one plant I was at recently has a policy that "all flow controllers will have tuning settings of X, Y, all level controllers will have settings of Z, W", etc, and it runs smoothly on those settings that they started up with. If something comes to the attention of the control engineer, it's likely to be multivariable, significantly nonlinear, or have some other unfriendly characteristic that renders many of the traditional techniques inapplicable. The best estimates you can obtain of plant dynamics are often very rough, and plant behaviour changes regularly, sometimes to a large degree. Noise is generally quite non-random, being mostly created by people through things like lineup switches and feedstock changes.
You get the picture. It's good to go through the bode/nyquist process to get a feel for what makes loops behave the way that they do, but IME their use in plants is pretty rare. If I had to take the exam I'd fail for sure.
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Bruce Varley
Thanks a lot.
There are a lot of textbooks available now with a lot of efforts in explaining the root locus plotting, which can be simply drawn using Matlab. It seems that the college professors should hold a meeting and discuss which is more useful for control systems teaching. Tell the student more about modeling and PID stuff.
I still remember that I have learned PID digital implementation, control loops in motor control in college, besides the root locus and Nyquist plot. I think perhaps those content may be reinforced.
Even the professors themselves have no actual experiences in real control system engineering. My professors are mathematicians and they surely pay no attention to the real world stuff. Perhaps college have to hire some real-world engineers to teach the college students.
Anyway, I passed the exam, then I need not revisit the ancient tools any more.
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Being able to sketch a simple root locus on a napkin is a good way to get an intuitive grasp of a system's behavior. Certainly, being able to interpret a root locus is good, and to really do that you need to push yourself beyond just sketching them on napkins -- either by doing dozens by hand, or by playing with hundreds on the computer.
The super-duper whiz-bang optimal control techniques require a lot of information about the system that you may not have. Ditto pole placement. So if you use them you need to either do some system identification and then some shading of what you tell the algorithm (Peter really likes pole placement, but when pressed he'll say things like "of course I don't try to place the poles _there_, the system would end up unstable!"). Robust control techniques take this into account, but you still have to know how much your parameters vary.
Which is all the long way of saying that out here in the real world, the tried-and-true techniques still have some currency. So do things like pole placement (or it wouldn't work so well for Peter) and robust design techniques. About the only thing that you've mentioned that isn't really current is plotting root-locus plots by hand -- and if you think while you do them, those lend you a life-long intuition for how a system behaves with changing parameters, so knowing the material is hardly a loss.
(Note: I rarely do root-locus plots on paper any more. But when I'd contemplating changes to a system, I'll often visualize simple ones in my head, sort of as a check plot on what I'm trying to do.)
Reply to
Tim Wescott
I thought the first part was funny.
I have studied Hinf and compared it with Kalman filters. It is just another= way to compute the corrective gains. There is no magic. One the whole th= e Hinf gains are just a little bigger than the Kalman gains. So what. I a= m not impressed.
What would impress me is updating the system transition matrix on-the-fly w= ith some continuous system identification.
I really prefer to model the system and then use the symbolic formulas to c= ompute the controller gains as I move the poles around. Usually I move the= m on the negative real axis to the left until there is some un-modeled limi= t. A big factor is feed back resolution and how the derivative gains make = the output look very noisy. I can also place the poles and the low pass fi= lter pole for the control output. This helps filter out the control output= noise ( I know it is due to non-linear feedback due to digital quantizing = ) but if you can trust your model it better to use an observer.
OT but still talking old techniques. I have a SOPDT temperature simulator.= I actually tried ZN for the first time ever to tune the temperature loop.= The results were bad considering the effort that it takes but the ZN techn= ique doesn't control so well on long dead times. It also takes a loooonnnn= g time to adjust the gain to find Ku when the cycle time of the oscillation= s is 12 to 13 minutes. There is lots of tweaking gains and drinking coffee= between the cycles but little up front knowledge or effort is required.
Along the same lines of the observer. I find that using a Smith Predictors= does wonders for temperature systems with a large dead time. I can even u= se FOPDT tuning on the SOPDT temperature simulator and it works reasonably = well.
Peter Nachtwey
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