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@ryanastiefes Dear Ryan,
I have used PIDlab at www.pidlab.com.

Hope this helps.

Best regards
Jaroslav

Hi Mike,
I see. In that case the simplest approach is to use the TSE block (three state element), which moreorless combines two comparators with hysteresis.

Kind regards
Jaroslav

Glad I could help!

Best regards
Jaroslav

Hi,
I recommend running an infinite loop in Matlab, reading a trigger (e.g. some Boolean value) from REXYGEN via REST API. Afterwards execute your function and pass the results to REXYGEN, again via REST API.

See example 0302-03 which is included in REXYGEN Studio.

Regards
Jaroslav

Hi Stepan,
thanks for reporting and explaining this. Since version 2.10.8 is outdated and there is no problem in the current version 2.50.9, we're not going to fix this. Please apply a workaround as you suggested. Thanks for understanding.

Kind regards
Jaroslav

Thanks for the explanation, now I understand. Nice solution.

Jaroslav

Hi Mike,
how about using the PWM block?

If u=-1, then UP will be ON and you can use it for heating. If u=0 then both outputs are off. And if u=-1 then DN will be ON and you can use it for cooling.

The PWM:offtime parameter defines the minimum delay between heating and cooling phases.

Let me know.

Kind regards
Jaroslav

Dear Ryan,
thanks for the parameters. If the controlled plant is defined by
K=0.059
del=540
tau1=3600
tau2=4200
then there is no need to use Smith predictor. A simple PI (k=16.859, ti=5866.1) or PID controller will work fine.

However, if you insist on using the Smith predictor, then the blocks inside the Smith_predictor subsystem should be as close to the controlled plant as possible. Because tau1 and tau2 are close to each other, you should use MDL function block as in the original example. And for sure, the only difference between the upper and lower block in the Smith_predictor subsystem should be the time delay. The gain and time constants must be the same.

The parameters which you provided can hardly lead to satisfactory closed loop performance.

Best regards
Jaroslav

Dear Stepan,
thanks for reporting this. I am able to replicate the problem. Which behaviour is correct? Is this something you have discovered while upgrading from 2.10.8 to 2.50.9?

Best regards
Jaroslav

Dear Ryan,
the task "Smith predictor - process model" is a model of the controlled process/plant. In a real-world application, you'll exclude the whole task from your project. In the control task, you'll replace the green pv Inport and mv Outport with I/O signals.

The Smith_Predictor subsystem in the control task is where you should change the gain, time constant and dead time to match your controlled process/plant. Plus I guess you'll want to replace the MDL blocks with FOPDT blocks.

Can you share the gain, time constant and dead time of your process? I understand the process response is quite slow, but it's the ratio between dead time and time constant which is the most important for the decision whether to use Smith predictor or not. There are many processes which are easy to control even with a simple PI/PID controller, no matter how sluggish their response is.

Best regards
Jaroslav