![rslogix 500 pid example rslogix 500 pid example](https://instrumentationtools.com/wp-content/uploads/2020/06/RSLogix-500-PID-block.png)
When setting up a PID loop initially, what values should you use as a starting point? This is a good question, and there is no hard and fast rule. The Kp, Ki, and Kd values need to be adjusted appropriately to achieve the desired response. Sometimes an overshoot is acceptable for a faster response, and sometimes the process is slow, so a slow response is best. That being said, each PID controller application is different and they need to be tuned accordingly to gain the best outcome. The response is not as stable as K = 0.5, and not as quick as K = 1.6, but this type of response is usually most appropriate and a good compromise for most applications. The controller with K = 1.1 reaches the setpoint quickly with minimal overshoot. It is usually not beneficial to have such an unstable response. This response is very fast but has poor stability. This condition is referred to as underdamped. The controller adjusts accordingly so that the output eventually settles down at the setpoint.
![rslogix 500 pid example rslogix 500 pid example](https://uploads-ssl.webflow.com/5e4c4a3ffa444d333b501251/5ef06d2169925ccf7ec01b30_AOx4t_F6TiysTumw_-vKhv_vTu_QnNs4YtRfe6Viy8D-alK9vS-p_x9AjY3L73PsUsjb5ETdor7zO-H194QFyb3zmunMreQTSZEbphWcxm5-LVvwRT4aMPNQngQ9QoUGtnIPkhxv.png)
The controller with K = 1.6 reacts fast and severely overshoots the setpoint. This response is very stable, but being so slow means it is usually not beneficial in most applications. This condition is referred to as overdamped. The controller with K = 0.5 reacts slowly and reaches the setpoint with no overshoot. Although these apply to most closed-loop systems, it is not a fit-for-all and some systems may respond differently. The table shows the general effects of each of the control parameters Kp, Ki, and Kd in a closed-loop system. The addition of a derivative term, however, has no effect on the steady-state error. This anticipation tends to add damping to the system, thereby decreasing overshoot. With derivative control, the control signal can become large if the error begins sloping upward, even while the magnitude of the error is still relatively small. With simple proportional control, if Kp is fixed, the only way that the control will increase is if the error increases.
![rslogix 500 pid example rslogix 500 pid example](https://usermanual.wiki/Ruckus/szvsze32clirgrevb20160628.2122883879-User-Guide-Page-1.png)
The addition of a derivative term to the controller, Kd, adds the ability of the controller to anticipate an error.
![rslogix 500 pid example rslogix 500 pid example](https://i.ytimg.com/vi/n-ToGbKllEA/maxresdefault.jpg)
This has the adverse effect of making a system sluggish and oscillatory since when the error signal changes sign, it may take a while for the integrator to unwind, leading to an overshoot in the opposite direction. If there is a persistent, steady error, the integrator builds over time, increasing the control signal and driving the error down. The addition of an integral term to the controller, Ki, helps to reduce the steady-state error. This is another way of saying that the response has reached a steady state. The steady-state error is the difference between the input and the output of a system as time goes to infinity. Increasing Kp also tends to reduce, but not eliminate, the steady-state error. This makes the controller react quicker for a given error in the closed-loop system, but it also means that it will likely overshoot more as well. Increasing the proportional gain, Kp, has the effect of proportionally increasing the control signal for the same level of error. u(t) is the controller output, and e(t) is the error value. The important parts of this equation are Kp, Ki, and Kd.