PID Kontrölde Formüllerin Anlamları?

[codabas1]

PID bloğu gayet doğru çalışmakta. Eğer matlabta simüle edip örnekleme zamanını kafadan sıkmazsan PID bloğunun net bir şekilde çalıştığını göreceksin.

Ayrıca Ziegler-Nichols hap çözümdür arkadaşlar. Simülasyon sırasında matlab system identification modülü ile sistemin modelini çıkarırken işimize de baya yaradığı olur.

örnekleme zamanı (dt) nasıl hesaplanıyor. benim tahminim toplam döngü işleme süresiymiş gibi geliyor ama emin değilim.

 

[elektronikmuh]

örnekleme zamanı (dt) nasıl hesaplanıyor. benim tahminim toplam döngü işleme süresiymiş gibi geliyor ama emin değilim.

Örnekleme zamanı; Siemens S7300-400 serisi PLC lerde; çevrim zamanı(programcı tarafından ayarlanabilen), PLC programından bağımsız olan organizasyon blokları var(OB35 gibi) bu OB lerin içerisine PID algoritması yazlıyor böylece dt değeri PLC çevrim süresinden bağımsız oluyor.

 

[sefaxxxxx]

Arkadaşlar ziegler nichols metodunda sistemin osilasyona girmesi gerekiyor.Ancak burada geri besleme sinyali mi osilasyona girmeli yoksa analog çıkış değerimi osilasyona girmeli bu konuda beni aydınlatabilir misiniz

 

[trakian86]

ziegler nichols metodu nu pratik te nasıl kullanırız? mesela motor hızı ile debi kontrolü, sıcaklık ile buhar vanası kontrolü gibi uygulamalarda?

 

[trakian86]

hocam bu dediğini nasıl yapacağız? biraz daha detaylı bilgi verirmisin?

sisteme birim basamak verip cevabını nasıl göreceğiz?

Ziegler Nichols ile çok başarılı sonuçlar alabilinir. Ziegler Nichols deneme yanılma dediğin şeyin yönteme dökülmüş halidir, sisteme makul bir Kp vererek başlar, sistemi osilasyona gitirip tekrar çıkartarak Ki ve Kd değerlerini bulursun. Kafadan deneme yanılmaya girip zaman kaybetmeni önler. İster deneme yanılma ister Ziegler Nichols olsun denemeleri yapabilmen için sistemin kritik olmayan (insan hayatı ve maddi bakımdan) ve kararlı bir sistem olması da gerekli tabi ki.
Yapabiliyorsanız Analog Input-Output kartlı bir PLC kullanıp sisteme birim basamak verip basamak cevabına bakabilir ve basamak cevabını excel'e çekip MATLAB'a atarak sisotool ile sistem transfer fonksiyonunu belirlersiniz. Daha sonra da PID kontrol paramatrelerini çıkartıp, ufak sabunlamalarla sisteme uygulayabilirsiniz.

 

[karaakrep_27]

s7-200 PID'de referans y'den küçük olduğunda kontrol işareti üretmemesinin sebebi nedir acaba bilen var mı?

 

[Bayram Karakaya]

Merhabalar,
PID kontrolle en kolay yöntem; (solda marka listesinden NotionControl seçiniz)
Programlanabilir DDC Modülleri Kategorisi ürünleri « Karakaya Teknoloji Bina Otomasyon Sistemleri[]=1&
Yazılım tamamen ücretsiz ve simülasyon imkanı sunuyor;
NC Tool v2.29 « Karakaya Teknoloji Bina Otomasyon Sistemleri
Yardım dosyası biraz bilgisi olan herkes için yeterli;
NC Tool Help File v3A « Karakaya Teknoloji Bina Otomasyon Sistemleri
Uygulamacı arkadaşlar kendilerini tanıtarak özel iskonto isteyebilirler,
Bilgilerinize,
Bayram KARAKAYA
Karakaya Teknoloji Bina Otomasyon Sistemleri

 

[morinel]

Kp dediğin bir katsayı bazında/Kd dediğin *derivative türev bazında/Ki dediğin integral bazda kontrol sağlayan terimlerdir.

 

[fkose]

Eyvallah kardeşim güzel anlatmışsın. Ama bunu sanayide nasıl kullanacağız onu söylememişsiniz.Kd=1 ise noluyor 2 ise noluyor, anlayamadım. bir transimiter ve vana dan oluşan bir düzenekte pıd nasıl çalışr hala anamış değiliz. Keşke anlatsan. Teşekkürler

 

[elektronikmuh]

• Download demo - 456 KB
• Download source - 18.8 KB

Overview

This code is a working example of a PID (Proportional, Integral, Derivative) control. This type of a control is used when processes change due to inertia. (A car's cruise control is a PID controller.) The PID algorithm is surprisingly simple, and can be implemented in five lines of code. There are three constants that must be determined in order to shape the control's output. The three constants as well as the set point and sampling interval can be changed in real time. The resulting shape of the output will be displayed in a strip chart.

How are PID loops used?

Many real world processes build up over time. When you step on the accelerator, your car moves slowly, then faster, and faster still, until you let off the gas pedal. As you speed up, you press the gas pedal less, then a little less, then even less, until you reach the speed limit. Unlike the digital world where things are either on (1) or off (0), real processes have varying degrees of on. In the driving example, how much the accelerator is turned on depends on the car's current inertia and how different the car's speed is from the speed limit. Controlling such a process with inertia can be done with only five lines of code. But, just like learning to drive, it takes practice to know if you are starting too quickly, or if you'll overshoot the speed limit.
Cruise control is one example of a PID control loop. To calculate the output, it needs three factors. The first, (P), is the difference between the current speed and the desired speed. The second, (I), is the sum of the differences over time. And, the third, (D), is the rate of change between sampled differences. Each factor is scaled by a gain constant; they are refered to as Kp, Ki, and Kd. The value of these gain constants determines how responsive the output will be. If the Kp, Ki, and Kd values are too high, the output (car's speed) will far exceed the set point (speed limit). Set too low, the output may never reach the set point (like driving 40mph on the highway).
Code implementation

In the real world, a process updates constantly. In order to simulate this action, a timer is used to run an equation that models the process. From a second timer, the PID control algorithm samples the process value at a rate slower than the process model updates. The Process Value or PV timer should run at least twice as faster than the PID control timer. In the application, the PV timer runs every 17ms, and the PID timer runs every 100ms.
The PV timer (tmrPV) tick event handler runs the following code:

//This timer updates the process data. it needs to be the fastest
// interval in the system.
private void tmrPV_Tick(object sender, EventArgs e)
{
   /* this is my version of cruise control.
    * PV = PV + (output * .2) - (PV*.10);
    * The equation contains values for speed, efficiency,
    * and wind resistance.
    * Here 'PV' is the speed of the car.
    * 'output' is the amount of gas supplied to the engine.
    * (It is only 20% efficient in this example)
    * And it looses energy at 10% of the speed. (The faster the
    * car moves the more PV will be reduced.)
    * Noise is added randomly if checked, otherwise noise is 0.0
    * (Noise doesn't relate to the cruise control, but can be useful
    * when modeling other processes.)
    */
   PV = PV + (output * 0.20) - (PV * 0.10) + noise;
   // change the above equation to fit your application.
}  The PID control timer (tmrPID_Ctrl) tick event handler runs:
  [ATTACH=full]83953[/ATTACH] Collapse | [URL="http://www.codeproject.com/Articles/36459/PID-process-control-a-Cruise-Control-example#"]Copy Code[/URL]
/* This represents the speed at which electronics could actually
* sample the process values.. and do any work on them.
* [most industrial batching processes are SLOW, on the order of minutes.
* but were going to deal in times 10ms to 1 second.
* Most PLC's have relatively slow data buses, and would sample
* temperatures on the order of 100's of milliseconds. So our
* starting time interval is 100ms]
*/
private void tmrPID_Ctrl_Tick(object sender, EventArgs e)
{ /*
   * Pseudo code (source Wikipedia)
   *
     previous_error = 0
     integral = 0
   start:
     error = setpoint  PV [actual_position]
     integral = integral + error*dt
     derivative = (error - previous_error)/dt
     output = Kp*error + Ki*integral + Kd*derivative
     previous_error = error
     wait(dt)
     goto start
   */
   // calculate the difference between
   // the desired value and the actual value
  error = setpoint - PV;
   // track error over time, scaled to the timer interval
  integral = integral + (error * Dt);
   // determine the amount of change from the last time checked
  derivative = (error - preError) / Dt;
   // calculate how much to drive the output in order to get to the
   // desired setpoint.
  output = (Kp * error) + (Ki * integral) + (Kd * derivative);
   // remember the error for the next time around.
  preError = error;
}

Take a look at some typical output shapes

In the images below, the green line is the set point, the blue line is the input (or process value), and the red line is the output (or manipulated value).

Correct control signal. All three gain constants are set correctly.

Overshoot. Integral constant too high.

Undershoot. Integral constant too low.

Ringing. Integral and Proportional gain constants too low.

Noise (under control). All three constants are set correctly.

Noise (not controlled). Derivative gain constant too high.
Code modifications you might consider

This example works for modeling any process that has inertia. The process being modeled here is a car's cruise control. In order to adapt the code to model a process other than cruise control, the equation in the PV timer tick event handler should be changed.
I use PID loops for electric furnace control. In furnace control, thermal mass is measured by sampling temperature. Better PID loop tuning results in efficient energy use. The code currently in the PV timer tick event handler is pretty close to a furnace equation. You can modify the equation to fit the system you want to model. As a rule of thumb, use the following equation:

ProcessValue = ProcessValue + (output * efficiency) – loss

In a real cruise control, there are limits on how much the output can change from sample to sample. This example does not include any way to limit the output's magnitude of the change. Such code might look like:

if ( (output – outputLast) > maxChange)
  output = outputLast + maxChange;
else if ( (outputLast – output) > maxChange)
  output = outputLast – maxChange;
outputLast = output;

end_if

The noise that can be added to the signal is not representative of anything your car might encounter. (It is really just a model of electrical noise.) Noise to a car's cruise control might be something like a hill, or a gust of wind. Both of those examples have a lower frequency than our noise. Since the noise is created in its own timer event handler, you can change the interval of the noise to change its frequency. You could also change the noise equation to model the effects of a hill, or even a chilling arctic blast.