PID Control Doubt: Tuning Proportional, Integral & Derivative Gains

In summary, PID controllers are useful in industrial applications to minimize error in a process. The P, I, and D values are tuned to make the set point and measured value the same. However, there is typically a deadband around the set point value. The tuning of these values can be done at various stages in the process, and there are different techniques for tuning discussed on wiki under PID.
  • #1
rama1001
132
1
Hello,
I have been obsessed with PID control concept and i want to notice you my doubt here. PID controllers are very useful in industrial applications to make the error free process(learned from the web). Basic PID controller mechanism is to tune the values P(Proportional gain), I(Integral gain from the recent errors) and D(derivative gain on about future errors) that are useful to minimize the error after the measured process. That means, the set point(SP)(before the operation) and the measure value(MV)(after the operation) should be same.

My concerns about,

i) If you tune P,I and D values to make both SP and MV same, Is that real process was done with some error plus set point value. Here i have been messed up and did not understand well how and where these tuning was done(at last stage of the process to just show SP and MV are same or starting of the process?).

Any help!

Thank you.
 
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  • #2
If I understand your question, yes, there is normally a deadband around the set point value. If you are asking how to tune a loop, there are various techniques discussed at wiki under PID.
 

FAQ: PID Control Doubt: Tuning Proportional, Integral & Derivative Gains

What is PID control and how does it work?

PID control, also known as Proportional-Integral-Derivative control, is a feedback control system used in many industrial and scientific applications. It is a mathematical algorithm that adjusts a control variable based on the error between the desired setpoint and the measured process variable. The Proportional, Integral, and Derivative terms work together to calculate and adjust the control variable in order to minimize the error and achieve the desired setpoint.

What are the main components of PID control?

The main components of PID control are the Proportional (P), Integral (I), and Derivative (D) terms. The Proportional term responds to the current error between the setpoint and the process variable. The Integral term takes into account the past errors and accumulates them over time to correct for any steady-state error. The Derivative term predicts the future trend of the error and helps to prevent overshooting or undershooting of the setpoint.

How do you tune the P, I, and D gains in PID control?

The P, I, and D gains can be tuned manually by trial and error, or by using a variety of tuning methods such as the Ziegler-Nichols method or the Cohen-Coon method. These methods involve adjusting the gains and observing the response of the system to find the optimal values that provide stable and accurate control.

What are some common challenges when tuning PID control?

One common challenge when tuning PID control is the trade-off between stability and performance. A high gain value may result in a fast response but can also lead to instability and oscillations. Another challenge is the non-linear behavior of some systems, which may require different gain values for different operating conditions. Additionally, the choice of tuning method and its parameters can also affect the performance of the PID control.

How can PID control be improved or optimized?

PID control can be improved or optimized by using advanced tuning methods, such as Model Predictive Control (MPC) or Adaptive Control, which take into account the dynamics of the system and adjust the gains in real-time. Additionally, implementing a feedforward control component can also improve the performance of PID control by anticipating and compensating for disturbances before they affect the process variable. Furthermore, using a combination of multiple control strategies, such as PID with fuzzy logic or neural networks, can also result in better control and improved efficiency.

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