According to the theory that I was taught, if your level controller cascades to a flow controller on the total flow out of a vessel, then this is an integrating process, if the response of the flow is much faster than the response of the level, a good assumption for the transfer funtion of the process would be kp/s. Building a software model of this system, I find that a P-only controller works very well and gives me steady state accuracy, i.e. I don't need integral control action. But if I do this in the plant, I find that I do need a PI-controller; a P-only controller does not control the level to setpoint. Why is this? Can somebody explain this to me please.
Because some thing is adding more fluid to your tank. Even if there is no error the pump must keep pumping fluid out at the rate the fluid is coming in so there is no net change and there error is maintained at the SP. Where does this extra signal come from if you are using only P gain and it sees no error? I bet you see an error in proportion to the rate the fluid is being added to the tank. You have found the answer. Use the I gain, It will windup to provide a control signal in proportion to the flow rate,
Because your model of Kp/s does not represent your plant.
You must just make a small modification to your model; add the varying inflow to the controller output before applying it to the integrator term Kp/s. Then you will find that the model, similar to the real plant, will also not give steady state accuracy using a P-only controller, and you will need the I-action for steady state accuracy.
PI Controller works well for level control, but use mainly P-action, even with very weak I-action the closed loop response is very good. There's nothing like strong I-action to make level control unstable very quickly.
How do you know? Is there an equation to justify your statement? I don't believe you just stepped in it after I gave the forum a warning about upgrading the quality of the posts..
OK, but what is strong?
You are right that the OP needs to modify his model. For one thing the gain is negative because increasing the pump speed makes the level lower. I have more but I will wait for replies.
John, if you want more information go to
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is a Scilab simulator script for your application at or near the end of the thread. Download Scilab at
Thanks for the comments Peter, I will try my best to help you.
You add a summer block with two inputs; one input from the controller output, and the other on representing the inflow. The output of the summer is then connected to the input of the pure integrator representing the vessel.
Good, we agree then.
To close the material balance around a vessel with a uncontrolled inflow and controlled outflow, the outflow must equal the inflow, then the level will be stable. This stable level is not necessarily at the required value; the setpoint could typically be 50% and this stable level is maybe 70%. In this scenario, proportional control action will do nothing, because the level is not changing, but integral control action will now change the outflow to make the level equal to the setpoint. But this is now causing the material balance to go unbalanced. Strong integral control action will cause strong material unbalance and this makes the closed loop unstable.
If you do a bode-plot of the pure integrating process, you will also see that. The P-action does not contribute to the -90 degrees phase shift caused by the process 1/s term, so, disregarding the other lags and dead times, P-action will never make an integrating-only process unstable (In practice there are of cause lags and dead times also, but still, you will find that with P-action, you can have very strong control action before the loop goes unstable). I-action, on the other hand, contributes another -90 degrees phase shift and with even the slightest additional lag and dead time (that you always get in real processes), the closed loop goes unstable very easily.
My experience over many years back this up too. In process plants I have found many LCs with strong oscillatory closed loop behaviour. I normally then just decrease the integral control action by a factor of
10, and increase the proportional control action by a factor of two, and nine times out of ten, the closed loop response is then significantly better. Try this.
Another way of looking at why you may need Integral Action (is offset a real problem in all cases?) for a Level Loop lies in the 'bias' value associated with the P-Only Controller.
The typical P-Only controller form is Output = BIAS + GAIN*ERROR.
For an integrating process (ie: a level loop), as mentioned earlier, is only steady at its balancing point, (outflow=inflow). If the BIAS value is set to the value of the controller output that makes the outflow (if controller based on outflow) equal to the inflow then a P- Only controller will not result in offset from SP. Now this is where the 'real-world' kicks in, since the inflow is a wild-stream, you would constantly have to change the BIAS to compensate, which is basically what integral action does, albeit in a much less intelligent fashion.
I have also seen papers on an internal feedback method (Sung and Lee (1996) and Arbogast (2006)) that basically use an inner controller to stabilize the non self-regulating process and an outer controller which now sees a self-regulating (stable) process; its basically a cascaded system where both controllers use the same process variable.
If you don't have enough integrator gain the tank will overflow before the motor winds up enough to match the flow. It would be a real shame to have the tank over flow when the flow is well within the capacity of the pump just because the integral action is too low. So what gain is is too high? We don't know becasue we don't really know what John's system but the formulas within the tanklevel.sce allow one to simulate what will happen one real numbers are entered for the plant. One should be able to predict the maximum error in the level that occurs as the flow goes from 0 to 100%. This would be handy because then well will know if the tank will overflow. Unfortunately I didn't include those computations in the tanklevel.sce. I also didn't plot the in flow rates but you can add that. Notice that in the tanklevel.sce there are some equations that calculate the PID gains as a function of the plant gain, time constant and a closed loop time constant. The tanklevel.sce program doesn't assume the pump will respond instantly so the tanklevel.sce is just a little more complicated. The response is supposed to be a critically damped response. Take a look at the integrator gain or time constant. It is calculated. The point here is that I don't have to fly by the seat of my pants and neither does John now.
So what model did I use? Bob, I know you know.
if you don't have the model then how do you make one a phase-gain/ nichols charts to prove the system is stable or unstable?
How do I just love this! I very nice technical challenge.
I am at the airport on my way to see a customer, so I can't do it right now. But I will stick my neck out and make a statement and say how I will test it. Maybe I'll be wrong, then I'll eat humble pie, but who's right or wrong is not really the issue, we both want to help John.
My statement is that your statement quoted above is wrong. Let me qualify that: if you have a steady tank with the level at setpoint and a disturbance is applied to the system, in the form of a change in inflow, with the level control on outflow (typical arrangement in process industries), P-only control will be able to give stronger disturbance rejection than PI-control. (This is not typical objective in the a typical process industries application)
I had a quick glance at the reference you gave above, so I'll try to proof my statement using your Scilab models (I might of course need to adjust it to reflect my scenario). I'll do it before Monday and get back to this forum (maybe I'll proof myself wrong, but I won't have a problem in admitting it, all I want is to help John)
I think I've done it. Thanks for the challenge Peter.
I have taken Peter's Scilab program from the reference earlier, and modified it to test disturbance rejection as opposed to setpoint tracking. My motivation for this is that there are many tank level controllers with the setpont fixed for years, but the inflow changes all the time. Given this scenario, response on inflow changes is more important than setpoint tracking.
Peter made a statement that I interpreted that with P-only control, the tank would overflow easier. So I tried to proof the opposite.
I used the Scilab simulation to get the proportional gain that will minimise level disturbance on a step change in inflow, with a constant setpoint, and then noted this max level variation, for different integral control time constants.
Results: Int time Gain for min level disturbance level disturbance (mm)
0.5 -7700 16.0
1.0 -8500 15.5 No -9200 15.2
Conclusion If you tune the controller for minimum level disturbance on a step change in inflow, then the more integral control action you use, the bigger the level variation.
Warning This is not a recommendation of how to tune a level controller. You need integral control for steady state accuracy, but a very small amount is sufficient. Strong control action is in many cases not a very good way to tune a level controller. Normally it's better to detune it to make use of the surge capacity in the vessel.
Pieter Steenekamp
Appendix: Peter's Scilab program as modified by Pieter for the above
// TANK LEVEL CONTROL Kp=-0.2368; // pump gain. (m^3/m)/CO% Tp=1/12; // plump time constant. minutes Kt=0.0279; // 1/(tank surface area) 1/m^2 Tc=0.25; // closed loop time constant. minutes //Kc=-605.444; // controller Gain. percent output per meter of error //Ti=3*Tc; // integrator time constant minutes Kc = evstr(x_dialog('controller Gain ?','-605.444')); Ti = evstr(x_dialog('integrator time constant minutes ?','0.75')); T=1/120; // simulation update period minutes PV(1)=2; // fluid level meters ui(1) = -10/Kp; // integrator contribution to CO flow_in = 10; // flow in. m^3/min CO(1) = -flow_in/Kp ; // control output. percent of full output flow_out(1) = -10; // flow pumped out. m^3/min maxLevel=0;
N=round(10/T); // CONVERT TO SAMPLE TIMES
for n=1:N; // simulate 10 minutes SP(n)=2; // set point meters
if n==10 then flow_in=20; end // calculate the rate of change in flow_out. This uses the simple differential // equation Tp*flow_out_dot+flow_outy=Kp*CO(n). Note flow_out is negative. flow_out_dot(n) = -flow_out(n)/Tp + (Kp/Tp)*CO(n); // calculate the new flow out by adding the rate of change times T flow_out(n+1) = flow_out(n) + flow_out_dot(n)*T; // integrate the difference between the flow in and flow out. PV(n+1) = PV(n) + Kt*(flow_in+flow_out(n))*T; if PV(n+1) > maxLevel then maxLevel = PV(n+1); end err(n) = SP(n) - PV(n); // calculate error up(n+1) = Kc*err(n); // calculate the proportional control output percent if Ti>0 then ui(n+1) = ui(n) + (Kc*T/Ti)*err(n); // calculate the integrator control output percent else ui(n+1) = ui(n); end CO(n+1) = ui(n+1) + up(n+1); // the control output is the sum of the integrator and proportional terms end PV($)=[]; // LIMIT TO 1200 ITEMS CO($)=[];
// PLOT THE SIMULATION // CREATE TO PLOTS USING THE SUBPLOTS FUNCTION // THE TOP PLOT SHOWS THE SP AND PV. THE BOTTOM PLOT SHOWS THE CO
t=T:T:N*T; // COMPUTE TIME INDEXES. START A TIME T IN INCREMENTS OF T TO N*T clf(); // CLEAR OR RESET THE CURRENT GRAPHICS FIGURE
subplot(2,1,1); // PLOT TEMPERATURES ON THE TOP PLOT plot(t,[SP PV]); xtitle('Tank Level Control Simulation','Time In Minutes','Tank Level'); legend("SP","PV"); subplot(2,1,2); // PLOT CONTROL OUTPUT ON THE BOTTOM PLOT
// PLOT THE CONTROL OUTPUT TERMS
plot(t,[CO]); xtitle('Tank Level Control Simulation','Time In Minutes','Control Output%'); legend("CO")
This is a good example of the problem with the tweak this gain here and tweak that gain here iterative approach to tuning. If you tweak the gains individually you are moving the poles. When you increased the integrator gain how did you affect the poles? Chances are you changed my critically damped response and changed it to something under damped that wouldn't respond well to disturbances. Hence your reaction is that more integrator gain is bad. The sad part is that you will believe this for the rest of your life. Now do this my way. First, run the original program. You will see the middle disturbance shows an error of about 90mm. Now ONLY reduce the closed loop time constant, tc, from .25 to .2 minutes and run the program again. Now you will see the disturbance caused error of just over
60mm. How can this be? According to you increasing the integrator gain should make the error worse. By making the close loop time constant smaller you moved the poles to the left on the negative real axis. If you look at the equation for Ti you will see that if was reduced from .75 minutes to .6 minutes which effectively makes the gain larger yet the error caused by the disturbance is MUCH smaller. You can reduce this error even more by making .1 minutes.
The point is that you must look at the end goal. The goal is the desired response, not a procedure for twiddling gains. The response is dictated by where the poles and zeros are. So the first sub goal is to move the poles and zeros to a place that will result in the desired response. The PID gains are just a method of getting the poles to the desired location. So what do you do? Change the PID gains one at a time and hope they get you to the desired goal or do you move the poles and then calculate out what the gains should be to achieve those goals. Even if you change one PID gain in the right direction, the poles may not move closer to the desired location and there resulting response will be worse. Therefore you will come to conclusions like you did that increasing the integrator gain is bad.
Lets take a detour back to the iterative tuning thread. Given the knowledge from that all gains should be changed together or then you can see that iterative tuning one gain at a time may not going to lead you to the best results unless you can find a way to change all the gains at the same time. If you looked at the article, to which I posted a link, you will see is based on changing all the gains at once by calculating the gradient of how the error changes as function of the gradient of each gain. From this calculates a vector or direction of change and there is a term that limits how much. This is a method. I just wish there was some that had actually implemented this method.
Finally, when I am twiddling gains I still have those equations in my head but I think about it differently that the rest of you. I think about what the system identifier did wrong. I can usually tell by looking at the graph of the estimate response to the actual response. I know how to change all the gains if a the estimated plant time constant is too long and the plant time constant should be shorter. I then think about how the a shorter plant time constant will change all the gains.
Thanks for the response Peter. I agree with you, but you're talking apples and I'm talking pears.
I can't but agree very strongly with this point; only by agreeing on the end goal can you compare apples with apples. I will mention three different possible end goals below.
First one is if you want to minimise the closed loop response time on setpoint changes. Here I would support your arguments and follow your design philosophy. Specific proportional and integral control tuning coefficients would give the "best" response, and my statement of "more integral control action is bad" would be wrong.
As an alternative consider a surge vessel where the objective is to maximise (as opposed to the above minimising) the loop response time on inflow disturbances, with the provision that you don't violate high or low limits. Although very typical, this is still not my apples. Let's move on.
Yet another end goal is simply to, starting from a stable system with no setpoint changes, to minimise the level deviation on inflow disturbances. With this being the case, and you tune the controller ending up with non-negative P- and I- settings, using any method you like, you can find other settings with less I-action that will give a smaller level deviation on the same inflow disturbance.
I used your program and model coefficients to demonstrate this "more integral control action is bad" point.
Yes, I am talking about being an engineer and doing a good job of calculating or at least estimating instead of guessing or trial and error.
No problem. If you can model the system you can do that too. How are you going to compute how much error is permissable?
I am beginning to think you are a lost cause. You didn't try my suggestion of reducing the Tc ( closed loop time constant ). As you say, increasing only the integrator is bad. When you do that the critically damped response becomes under damped. I have said that before and I am getting tired of repeating myself. The gains should be moved together because the goal is to move the pole(s) in the desired direction, not tweak a gain.
One more chance. Change the closed loop time constant to .2 minutes and see the difference. Note that changing the closed loop time constant changes all the gains, not just the integrator time constant. That is because changing the closed loop time constant actually move the poles along the negative real axis.
It's interesting, that's exactly how my wife describes me too. And then also, ever since primary school, I was one of the slow learners in class, I haven't been very quick to pick up complex mental things. So if you don't mind taking it slowly - let's leave the pole placement and theoretical stuff for now, and just show me an example to proof your point, if you can of course!
Please give me a Scilab (or any other "free" package, to share it with the world, it's maybe unfair to expect the world to use an expensive package) program, representing a tank with a varying inflow, level control on the outflow, starting from steady state with the level at setpoint and a step change in inflow. Then give us the PI controller tuning, with greater than zero integral action, that will minimise the maximum level error, and I will respond by taking your model coefficients and your program and reduce your integral control action, use my practical, no theory, iterative tuning, and the result will be smaller maximum level error, for the same inflow disturbance. How's that for a challenge!
I have done a similar exercise to demonstrate my point. I have taken Peter's program from the reference given above, changed the configuration to reflect the said scenario, and found the proportional control for different integral control settings to minimise the maximum level error. My conclusion was that the less integral control, the smaller the maximum level error. Please refer to a previous posting in this thread. If you don't agree, show me my error please.
The scenario I explained is not too uncommon - there are many (maybe not most applications, but still not too uncommon) tanks where the main objective of the level control is just to keep it from overflowing or emptying. Tuning based on this objective will be good for these applications. (Again, this is NOT valid for all level controllers, this is a special case only)
I don't particularly care about me, feel free to call me names if makes you feel better, but I'd like to continue this to some conclusion for the other readers of this forum. So the challenge is not only for Peter, but any other reader of the forum.
In the meantime my advice to the control world is that, given the said objectives, less integral control is better. I do realise that even hundred positive examples won't proof it, but one negative will proof it wrong, but still, as a demonstration of my point, refer to the Scilab program with different tuning settings that I gave earlier in this thread.
Thanks to all the contributors for all the comments; they are very helpful.
Pieter's conclusion seems to be valid that for level control, it's better to use less integral action.
I have taken his Scilab program and tried the three different tuning sets provided. This does indicate that, for this model in any case, on inflow disturbances a smaller integral control time constant (less integral action) results in a smaller maximum level deviation. (It seems like he used iterative tuning to find the best proportional action for each different integral time). I have also tried some other values and they confirm the general relationship of less integral action resulting in smaller level deviation.
We have one application like this on our plant where the pump often trips on low tank level and I have tried, without spectacular success, tuning the LC to prevent this. I now realize that my integral control action is probably too strong. I will go and try a combination of weaker integral and stronger proportional control to prevent low level trips. Fortunately, on this application, a sharp drop in feed, resulting in a low level (and pump trip), is never followed closely by another sharp drop in feed, so it's not required to recover the level to setpoint fast after a drop in feed; in fact it's probably good if it then stays low. We have one very constant flow going into the tank, and another one that changes between low and high flow. The issue is simply to prevent a low level trip.
This does raise the following questions though: a) Is this Scilab model a good representation of a tank level arrangement in real plants? b) Are there other typical tank level arrangements for which this conclusion is not valid? c) Would it also be valid for the control structure of a LC cascading to a FC that we typically use on our chemical plant?
That is only because you didn't follow the thread.
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modified the program so that it proved his point by disabling the the code that computed the PID gains by just changing the closed loop time constant.
First, it isn't his program. He modified mine, you would know that if you follow the thread. He removed the code that would prove my point and inserted his own so he could play his trial and error games.
and tried the three different tuning
A smaller time constant results in more integral action
results in a smaller maximum level deviation. (It
If you look at the original program you will find the opposite.
No, it is probably a poorly implemented PID. Again look at the orignal program and change the Tc ( closed loop time constants ) from .25 to .1 minutes. Change the final flow_in to 0. Now try the Tc at 0.25 ( Ti=.75) then try Tc=0.1 ( Ti=.3). You will see the level stops receding faster when the Tc=0.1 and the integrator time constant is smaller ( higher gain ). At this point I am not sure if Pieter isn't confused ,like you are, about the relationship time constant and the gain. Longer integrator time constants result in lower integrator gains.
I will go and try a combination of
Read the thread. The problem was submitted by a young engineer in Poland.
Yes, the pump can be on the inlet or the outlet.
Yes, but I doubt that your flow controller can change the flow rate instantly. SIGreg in the thread said his pump definitely had a time constant and took time to change speeds. The model in the
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thread assumes the pump or flow controller has a time constant.
Before Pieter lures you to the dark side you really should look at the whole thread and download the program that is there. It is obvious to me that Pieter has not followed my advice. He never tried just changing the Tc so that all the gains are changed at once.
thread.http://www.plctalk.net/qanda/showthread.php?t=26395&highlight=sigreg> Pieter modified the program so that it proved his point by disabling the
that, for this model in any case, on
iterative tuning to find the best proportional
Please help me.
I tried to follow your recommendations very carefully, but the response does not look like it reflects my problem; the PV is not equal to the SP when the run starts.
So I changed the SP(n)=0 and kept the flow_in = 20. This does seem to reflect my problem. It is also very typical in the chemical process industry where I work; the PV=SP for long periods with everything else also steady. Then there is a sudden inflow upset. I'd like the simulation to reflect this scenario. And this is the way Pieter described his scenario and his modifications to your program seems to reflect this too. Your's do not.
I then ran your program with above mod to reflect my problem and tested your two recommended values, and then also ran it with Pieter's recommended tuning of P=only control
Your first recommendation: Tc=0.1 Max level deviation = 0.1081467
Your second recommendation: Tc=0.25 Max level deviation = 0.1208479
Pieter's recommended tuning Kc = -9200 (no integral action): Max level deviation = 0.0440022
So is the level lower or higher than the set point? If the level is much higher then the pump will be on 100% until it approaches the SP If the level is lower the pump can do nothing. It must remain off. Try changing my program so that PV(1)=2.5 and then PV(1)=1.5
Notice that the pump gain is only -0.2368 m^3/min/%CO. At 100% this example can only pump 23.68 M^3/min. It would be close to the maximum capacity and couldn't get the level down to the set point very quickly. Does your pump work at maximum capacity very often?
It is also very typical in the chemical process
What kind of disturbance do you want? Change the in_flow values on my program to reflect the disturbance you want.
And this is the way Pieter
GET MY PROGRAM and don't bother to comment until you do! You have been led astray by sloppy programming. Pieter obviously didn't understand my trick to limited integrator wind up so he removed it which will make using an integrator look bad. My program changes the in_flow as a function of time to reflect disturbances. If you had run my program you wouldn't make false statements like mine program does not reflect disturbances. Read the original thread on PLCs.net. Pieter removed some of the vital parts of my program like the limit on the output of 0 to 100%. I think I can get better results if I didn't have to worry about details like that. He also removed the derivative gain or time constant and that is required to keep a critically damped response and avoid overshoot. One more thing that he did is that his disturbance goes from 10 to 20 m^3/min. Whereas mine goes from 5 to 20m^3/min
My program didn't calculate the max level deviation. Did you add it or are you using Pieter's messed up version.
0.029769 That beats Pieter's easily even though my disturbance goes form 5 to 20 m^3/min instead of just 10 to 20.
On my program it is 0.091695 even with the 5 to 20 m^3/min disturbance.
WHY ASK ME? YOU AREN'T DOING WHAT I ASK ANYWAY! YES, YOU USED PIETER's PROGRAM!!! I told you were to get my program and to read the thread.
When I run Pieter's program with Kc=-9200 it oscillates. I doubt the even the Tc as low as 0.1 minutes is practical. Think about this, if the gain is -9200 that means that 1 mm of ripple in the tank will cause the output to change by 9.2 percent. Quantizing and noise will also be a problem.
Let me add my two cents to this discussion. Assume that the process can be modeled as Kp/s and we control the flow out with a linearized flow control valve (just to make the problem simple) using a standard ISA PID controller. The tuning will be for disturbance rejection - where the disturbance is a change in the inlet flow. Let us look at the root locus sketch as the proportional gain increases from zero to infinity for a fixed integral gain. There will be two poles at the origin for Kc = 0. As Kc is increased, the closed loop poles move into the left hand plane and the sketch forms a circle. The two poles converge on the negative real axis when Kc = Ki/15Kp. Ki is in repeats per minute and Kc is the proportional gain and Td = 0. These poles will be at -KpKc/2. If Kc is increased further, the two poles travel on the negative real axis (one toward the origin and the other toward - infinity. For Kc less than Ki/15Kp, the response will be under damped. It is critically damped for Kc = Ki/15Kp. For Kc greater than Ki/15Kp, the response will be overdamped. If you want it be critically damped, you would have to adjust Kc every time you adjusted Ki. However if the Kc is large so that you are overdamped, you can tweak Ki without adjusting Kc (as long as the poles stay on the real axis) and this is what I often do.
The concept of Allowable Level Variation (ALV) simplifies tuning of the level controller. First you must do an open loop bump test to get an estimate of Kp. The ALV is the maximum variation from setpoint that you will allow under regulatory operation. It is expressed in %. Use the largest ALV possible so the tank can absorb the disturbace. The tuning equations are: Kc = 100/ALV, Ki = 15KcKp (for critical damping). Then decrease the Ki some to make the response more robust. During my 30 year career in the Pulp & Paper Industy, I used this method on hundreds of tank level controllers.
"Peter Nachtwey" wrote in message news:zNKdnW-JOoJazYTbnZ2dnUVZ snipped-for-privacy@comcast.com...
I am very happy to confirm that, yes it is true that, if you start a level from steady with level at required value, then the input floe changes, and you are not concerned about the level coming back fast, it is better to use only proportional control. That is because I have been doing it for years with good success, and just confirmed it using different models. I normally do add at least a very tiny bit of integral control; maybe use an integral time constant of 1000 times the time it would take the tank to empty from full with no in-flow and maximum controller output. In really difficult cases, when the "actuator" behaves like it has significant dead time, I have been forced to use even longer integral time constants to achieve acceptable response. (I use inverted commas, because it is mostly the process arrangements causing this apparent dead time and not directly the actuator, but for modeling purposes you can just as well model it as though the actuator is sluggish)
I followed this thread and did do testing, and found, yes indeed, the simulations confirm my experience. If you have a difficult level control, prone to level reaching high and/or low limits, and you are not worried with other things, like changing the controller output fast, or the level actually at SP, then indeed if you use less integral control you can then use stronger proportional control without level going unstable, and this will give you less risk of level tripping on low or high limit. Especially if an increase in-flow is not normally followed shortly by another increase in in-flow. This will make it that if you "caught" the level from violating the limit with proportional control, very weak integral action is sufficient to bring to back to SP very slowly.
I read Mr. Peter Nachtway concerns that they did not use his program so I check. So I see Mr. Peter Nachtway's program does not model the issue at hand. So I said, no problem it is very easy to make very small change so that the program of Mr. Peter Nachtway does indeed model the issue at hand. I was very careful not to change the other things that he said Mr. Pieter Steenekanp changed. Please look below for the program after I made the changes.
Please note Mr. Peter Nachtway, I did not use Mr Pieter Steenekanps program. I copied the one in the reference you gave in the thread and modified it, using absolutely minimum modifications, just to test the response from steady state when the in-flow changes. Please don't get mad at me too! I am already shit scared to write this posting, but I think I owe it to the other people to share my experience and testing results using your program.
So I also download Scilab. This is a very nice package. I then also build other models using the Scicos application in Scilab to test this statement and found it to be generally true. There might be some strange combinations or conditions that makes it untrue, but consistent with my doing it practically and testing many, including Mr. Peter Nachtway's (see below) models, I am very confident that it is true and valid and a good practical tuning method.
Of course, in Mr Peter Nachtway's this model - do not interpret the tank level as the actual level, consider it to be the changes from steady state. You can change the program of course to reflect actual level, but when Mr. Pieter Steenekanp did that, Mr Peter Nachtway got mad, so I rather make minimum changes to Mr. Peter Nachtway's program. I don't like it when Mr. Peter Nachtway get mad.
But on the other hand, this proportional-only control using a gain of
-9200 is indeed very strong, with a strong under-damped, but still not unstable, behavior. So I reduced the proportional gain, but the maximum level is still smaller than if you have any integral control.
But on the other hand, this model is "very kind" to some difficult tank level controls in the chemical industry. So I build a Scicos model of tank level control, but instead of the pump having just a single lag, I added two more lags, also including some dead time, to make the closed loop control "not so very kind". And I changed the coefficients to try to duplicate the problem that Mr. John Jay described. With Mr Peter Nachtway's model coefficients you will very easily prevent a low level trip. So there must be some other combination of model parameters replicating this problem. So I found this by adding apparent dead time to the "actuator" and made the tank surface area smaller.
And indeed, I also find that for "not so very kind" applications, if the tank is prone to empty on an in-flow disturbance, and if there is a more sluggish response (like in 3 lags in series, or dead time) included, the conclusion is not only valid, but it translates to very practical way of tuning the level control. Although it is true for Mr. Peter Nachtway's coefficients, this represents a "very kind" level control and it does not really make much of a difference. Although the statement is true, it does not translate to practical advice unless you start with a level control that is difficult to tune to prevent level trips.
Please Mr Peter Nachtway, don't get mad. Just give us a model to show us that we are wrong. Don't say we must read your thread, because your thread is very helpful for another application, not this one. Just show us your model, representing a level control starting from steady state, with the level at SP. Then test the response on a change in in- flow. Then you try it out with your PI control, and then also with the P-only control giving the smallest level deviation. If you find that PI-control does better, try to increase the gain of the P-only control, until you get to the point where it goes unstable and does indeed not decrease the maximum level deviation further. If you find the biggest gain for the proportional-only controller so that if you increase the gain further, the maximum level deviation increases, and this maximum gain for the proportional-only control gives a maximum level deviation that is larger than the maximum level deviation of your PI-controller with non-negative integral control action, then you will have proofed your point. If you achieve that I would say: well done Mr. Peter Nachtway, you taught me something very useful, thank you very much. And I will go and find where did I go wrong.
Thank you very much
Lu Hing
// Mr. Peter Nachtway's program with very small modifications by Mr. Lu Hing // to represent the case where you start from steady state // with level at required value and there is a change in in-flow.
// TANK LEVEL CONTROL
maximumLevel=0; // added by Mr. Lu Hing to check the maximum level
Kp=-0.2368; // pump gain. (m^3/m)/CO% Tp=1/12; // plump time constant. minutes Kt=0.0279; // 1/(tank surface area) 1/m^2 Tc=0.25; // closed loop time constant. minutes Tc=0.1; // closed loop time constant. minutes Kc=-605.444; // controller Gain. percent output per meter of error Ti=3*Tc; // integrator time constant minutes
// For Mr. Peter Nachtway's tuning keep the following commented out, to test proportional only, delete the comment signs // Ti = 1000000 // added by Mr. Lu Hing to practically remove integral so to check proportional control only // Kc = -9200 // added by Mr. Lu Hing to check strong proportional control
T=1/120; // simulation update period minutes PV(1)=0; // fluid level meters CO(1)=0; // control output. percent of full output ui(1) = 0; // integrator contribution to CO flow_in=20; // flow in. m^3/min flow_out(1)=0; // flow pumped out. m^3/min
N=round(10/T); // CONVERT TO SAMPLE TIMES
for n=1:N; // simulate 10 minutes
SP(n)=2; // set point meters SP(n)=0; // added by Mr. Lu Hing to check max level on inflow disturbance from steady state and not from SP change
// calculate the rate of change in flow_out. This uses the simple differential // equation Tp*flow_out_dot+flow_outy=Kp*CO(n). Note flow_out is negative. flow_out_dot(n) = -flow_out(n)/Tp + (Kp/Tp)*CO(n); // calculate the new flow out by adding the rate of change times T flow_out(n+1) = flow_out(n) + flow_out_dot(n)*T; // integrate the difference between the flow in and flow out. PV(n+1) = PV(n) + Kt*(flow_in+flow_out(n))*T;
// added by Mr Lu Hing to test the maximum level deviation if PV(n+1) > maximumLevel then maximumLevel = PV(n+1); end
err(n) = SP(n) - PV(n); // calculate error up = Kc*err(n); // calculate the proportional control output percent ui = ui + (Kc*T/Ti)*err(n); // calculate the integrator control output percent if ui > (100-up) then // limit the integrator contribution to the control output ui = 100-up; elseif ui < 0-up then ui = 0-up; end CO(n+1) = ui + up; // the control output is the sum of the integrator and proportional terms end PV($)=[]; // LIMIT TO 1200 ITEMS CO($)=[];
// PLOT THE SIMULATION // CREATE TO PLOTS USING THE SUBPLOTS FUNCTION // THE TOP PLOT SHOWS THE SP AND PV. THE BOTTOM PLOT SHOWS THE CO
t=T:T:N*T; // COMPUTE TIME INDEXES. START A TIME T IN INCREMENTS OF T TO N*T clf(); // CLEAR OR RESET THE CURRENT GRAPHICS FIGURE
subplot(2,1,1); // PLOT TEMPERATURES ON THE TOP PLOT plot(t,[SP PV]); xtitle('Tank Level Control Simulation','Time In Minutes','Tank Level'); legend("SP","PV"); subplot(2,1,2); // PLOT CONTROL OUTPUT ON THE BOTTOM PLOT
// PLOT THE CONTROL OUTPUT TERMS
plot(t,[CO]); xtitle('Tank Level Control Simulation','Time In Minutes','Control Output%'); legend("CO")
maximumLevel // added by Mr Lu Hing to display tha maximum deviation
Why not? Be specific. You can see that my program changes the inflow.
You removed the derivative term. That invalidates the test.
I am already shit scared to write this posting, but I
If you read the original thread you will see that is the model of a real system provided to us by an engineer trying to model his system. I don't understand why you would complain about the model. It was good for the engineer on
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My program changes the in_flow variable 3 times to simulate disturbances. I should probably add another graph that shows the in flow levels just to make that clear.
Provide us with another one if you don't like that provided. From what I see both Pieter and Lu Hing have both miss represent the tuning I use because they have modified the program.
Mr. Lu Hing, what about the derivative time constant or derivative gain? Your 'small change' got rid of the derivative time constant. Well? All the gains must 'work together' to achieve the desired response. You effectively sabotaged my controller and made it look bad. What is wrong with you guys? You seem to pick and choose the gains you want to use and ignore the ones you don't with out any concern as to how the gains work together. See the other thread on iterative tuning.
Here is a link to where I going to keep news group data. ftp://ftp.deltacompsys.com/public/NG
There are two .gif files. One showing Pieter's tuning and the other showing my tuning with the Tc set to 0.1 minutes. Which would you rather use. Both have an max error of about 0.030 meter. My tanklevel.sce is in the same directory. It has only been modified to add Pieter's tuning and a max_error variable was added. If you run my program you will see my tuning provides a slightly smaller peak but the average error and wear and tear on the pump is much less. Which one would you use?
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