IMC-Based PID Controller Design for Second Order Process - Homework Solution

In summary, The tuning parameters for a second order process are given by ##k_{c} = \frac {\tau_{1}+\tau_{2}}{k_{p} \lambda}##, ##\tau_{I} = \tau_{1} + \tau_{2}##, and ##\tau_{D} = \frac {\tau_{1} \tau_{2}}{\tau_{1} + \tau_{2}}##. The process time constants are -0.7906 and -5.2444, and the controller parameters are 22.0658, -6.0350, and -1.4556 for ##k_{c}##, ##\tau_{I}
  • #1
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Homework Statement


upload_2015-11-3_14-44-10.png


Homework Equations

The Attempt at a Solution


Hello,
I know for a second order process, the tuning parameters are given as ##k_{c} = \frac {\tau_{1}+\tau_{2}}{k_{p} \lambda}##, ##\tau_{I} = \tau_{1} + \tau_{2}##, and ##\tau_{D} = \frac {\tau_{1}+\tau_{2}}{\tau_{1} \tau_{2}}##
Code:
syms s
lambda = 1;
kp = -0.2735;
gp = kp/(s^2+6.035*s+4.146);
[num,den] = numden(gp);
factors = eval(solve(den,s));
tau1 = factors(1); tau2 = factors(2);
kc = (tau1+tau2)/(kp*lambda)
tauI = tau1+tau2
tauD = (tau1+tau2)/(tau1*tau2)

These are my process time constants
Code:
tau1 =

  -0.7906

tau2 =

  -5.2444
This gives my controller parameters
Code:
kc =

  22.0658tauI =

  -6.0350tauD =

  -1.4556

I go into simulink, and here is my model
upload_2015-11-3_14-51-0.png

And here are my PID controller inputs
upload_2015-11-3_14-51-28.png

But I haven't figured out why my controller is not working, here is the output
upload_2015-11-3_14-58-26.png
 

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  • #2
I realized that ##\tau_{D} = \frac {\tau_{1} \tau_{2}}{\tau_{1} + \tau_{2}}##, but still my output is not correct even after changing my ##\tau_{D}## term

upload_2015-11-3_17-43-48.png
 
  • #3
I figured it out. My time constants were not correct or in the right form
 

FAQ: IMC-Based PID Controller Design for Second Order Process - Homework Solution

What is an IMC-based PID Controller?

An IMC-based PID Controller is a type of control system used in industrial processes to regulate a specific output variable. It combines the traditional Proportional-Integral-Derivative (PID) controller with Internal Model Control (IMC) techniques to improve control performance and robustness.

How does an IMC-based PID Controller work?

The IMC-based PID Controller uses a mathematical model of the process, known as the internal model, to predict the future behavior of the system. This model is then used to adjust the control signal in real-time, taking into account the system's dynamics and any disturbances, to achieve the desired output.

What are the advantages of using an IMC-based PID Controller?

An IMC-based PID Controller offers several benefits over traditional PID controllers, including improved control performance, robustness to disturbances and model uncertainties, and the ability to handle non-linear and time-varying processes. It also allows for easy tuning and does not require extensive knowledge of the process dynamics.

What are the limitations of an IMC-based PID Controller?

One limitation of an IMC-based PID Controller is that it requires a good mathematical model of the process. If the model is inaccurate, the controller's performance can be affected. Additionally, the tuning parameters may need to be adjusted for each specific process, which can be time-consuming.

In what applications is an IMC-based PID Controller commonly used?

An IMC-based PID Controller is commonly used in a wide range of industrial processes, such as chemical plants, power plants, and manufacturing facilities. It is also used in various control systems, including robotics, automotive, and aerospace industries.

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