PID Control System Design and Automatic Tuning using MATLAB/Simulink. Liuping Wang

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PID Control System Design and Automatic Tuning using MATLAB/Simulink - Liuping Wang


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the choice of images basically led to a pole-zero cancellation in the control system. Such a pole-zero cancellation will limit, as discussed in the next chapter, the control system performance in the disturbance rejection, particularly when images is large4. In Skogestad (2003), the IMC-PID tuning rules are modified to reduce the integral time constant as

      (1.48)equation

      The IMC-PID controller tuning rules are also extended to integrating systems in Skogestad (2003). Although the system has an integrator as part of its dynamics, integral control is still required for disturbance rejection (see Chapter 2).

      Assuming that the system has the integrator with delay model:

      (1.49)equation

      then a PI controller is recommended with the following parameters:

      (1.50)equation

      If the transfer function for the integrating system has the form:

      (1.51)equation

      then a PID controller is recommended to have the following parameters:

      (1.52)equation

      If the system has a double integrator with the transfer function

      (1.53)equation

      then a PID controller is recommended with the following parameters:

      (1.54)equation

      The IMC-PID controller tuning rules will be studied in Examples 2.1 and 2.2.

      

      1.4.2 Padula and Visioli Tuning Rules

      Several sets of tuning rules were introduced in Padula and Visioli (2011) and Padula and Visioli (2012). These tuning rules are based on the first order plus delay model:

equation
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      They were derived using optimization methods for minimizing an error function together with the sensitivity peak in the frequency domain (see Chapter 2).


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