Add the second feedback loop with discrete-time PID controller as shown in the Xcos diagram in Figure 1, or download dpidsim.zcos. The plant consists of a robot joint driven by DC motor and a LPF at its input. We will use the setup in Figure 10 from our Module 4: PID Control. We want to simulate how this controller performs compared to its continuous-time version. Obviously for all the terms above, the sampling period affects the gains of integral and derivative terms.Īs an example, suppose we use backward Euler methods for both the integral and derivative terms, the resulting discrete-time PID controller is represented by Similarly, the derivative term in (3) can be discretized as Given a sampling period Ts ,the integral term Ki/s can be represented in discrete-from by There are commonly 3 variations to do so, by means of forward Euler, backward Euler, and trapezoidal methods. It is quite common to modify the derivative term to an LPF filter, to make it less noisyĪ straightforward way to discretize this controller is to convert the integral and derivative terms to their discrete-time counterpart.
In this article we investigate such relationship on a commonly-used PID form.įor the continuous-time PID, we start with the so-called parallel form In practice, we may want to relate a chosen set of parameters in continuous-time, perhaps from simulation or some tuning rule, to its discrete-time representation. In that article, we simplify the matter by omitting the effect of sampling period on the PID parameters. In our previous article Digital PID Controllers, we discussed some basics of PID controller implementation as software algorithm on a computer. Part I: Discrete PID Gains as Functions of Sampling Time This content was kindly contributed by Dew Toochinda, the Scilab Ninja, and originally posted on
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