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

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Название PID Control System Design and Automatic Tuning using MATLAB/Simulink
Автор произведения Liuping Wang
Жанр Отраслевые издания
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Издательство Отраслевые издания
Год выпуска 0
isbn 9781119469407



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alt="images"/>; line (2) tuning rule with images.

       With the same sampling interval , and both proportional and derivative control on output only (IPD structure) where the derivative filter time constant is selected as , the closed-loop responses are simulated. Figure 1.20 compares the closed-loop responses. Both tuning rules lead to stable closed-loop control systems. It is seen that there are overshoots in both reference responses, which was caused by the quite large derivative gains.

      1.5.2 Fired Heater Control Example

      (1.58)equation

      and at the high fuel operating condition,

      (1.59)equation

      where the time constant is in minutes. Note that there are dramatically differences in time delay and the steady-state gain of the transfer function models.

       In this example, we will show how to use the tuning rules to find the PID controller parameters for the fired heater at the lower operating condition using the transfer function (1.58) and simulate the closed-loop response with a step reference signal using sampling interval (min) and a negative step disturbance entering at the half of the simulation time.

       The higher operating condition case is left as an exercise.

      Solution. Figure 1.21 shows the unit step response with the lines drawn to identify the time delay and time constant for a first order approximation. From the graph, the time delay is found as 9.54 min and the time constant min. With the steady-state gain equal to 3, the approximation using first order plus model leads to the following transfer function:

      (1.60)equation

       Now, applying the Ziegler–Nichols tuning rules (see Table 1.2), Cohen–Coon tuning rules (see Table 1.3) and Wang–Cluett tuning rules (see Table 1.6), we obtain the PI controller parameters for the fired heater process shown in Table 1.9. The PI controller parameters obtained are drastically different. The PI controllers using Ziegler–Nichols and Wang–Cluett tuning rules produce stable closed-loop system for the fired heater process, however the PI controller using Cohen–Coon tuning rules does not lead to a stable closed-loop system, which was verified using closed-loop simulation. To evaluate the closed-loop control performance, a unit step input signal is used as a reference and a step input disturbance with magnitude of is added to the closed-loop simulation at half of the simulation time. Figure 1.22(a) shows the control signals generated by the PI controllers and Figure 1.22(b) shows the output responses to the reference change and the disturbance signal. Both closed-loop systems have oscillations, but in comparison, the controller using Wang–Cluett tuning rules leads to a slightly better closed-loop performance with less oscillations.

Graph depicting Time on the horizontal axis, and two curves plotted for Unit step response of the fired heater process.
images images
Ziegler–Nichols 0.4239 28.6200
Cohen–Coon 0.4517 13.2353
Wang–Cluett 0.2835 15.7610
Image described by caption and surrounding text.

      Several sets of tuning rules have been introduced in this chapter. These tuning rules are very simple and easy to use if the system can be approximated by a first-order-plus delay model. However, they offer no guarantees on the closed-loop performance as demonstrated by the simulation examples. Some of the examples used in this chapter will be analyzed using the Nyquist stability criterion and sensitivity functions in Chapter 2.

      1 Text books in control engineering