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And third, given the power of the current computational technology running all your control loops at a fast rate, say 500 milliseconds or quicker, will make disappear pretty much all the problems associated to sampling in process control.
As for your questions: Executing the loops less often is in general a bad practice from the control point of view, as you are only increasing the apparent dead time, which will call for a detuning of your controller anyway.
Slowing the execution time of a loop does not reduce the oscillation of any loop; it will worsen it.
A: First, you will NEVER change the dead time of a process loop simply by changing the tuning parameters of the controller. Dead time is a function of both how soon the process responds to changes to the manipulated variable and how soon the measurement sensor is able to recognize such a change.
Unfortunately, the traditional PID control loop is inadequate to properly to respond to dead time, as it is a reactive response, only responding to changes in error (SP-PV), rather than a pro-active response. So, you can fuss over scan rate or integral action all you want. Both of these are reactive solutions, and just slow down the effective response.
So, de-tuning a process control loop just slows down the controller response to match the natural response of the process.
A better strategy is to consider some pro-active solutions:
What is causing the dead time? Take time to understand the process. Dead time is the time it takes from when the input to the process changes, to when it is detected. Actually, re-locating the measurement sensor can significantly reduce the dead time.
The classical pro-active solution for dead time is the Smith Predictor, an addition to the PID algorithm where you predict the behavior of dead time response and configure this into your solution. Unfortunately, not many control algorithms offer a Smith Predictor solution.
Configure a feed-forward solution. Here, assume you predicted a response to change to a controlled variable or change in load. You can often collect this data from historical logs from your control system. Now, include this in your response. Also here, most PLCs or DCSs offer no pre-configured solutions. But, if you know and understand the cause and effect relationship, feed-forward is as simple as including an X-Y characterizer function.
For more complex processes, consider a model-predictive control (MPC) solution. Often, this requires purchase of specific MPC solution software. Depending on cost, this might be the optimal solution.
Should you like to learn more, please call or email with questions or comments.
A: PID loop is a misnomer we all use. PID is a block in a system diagram. The controller generates a signal based on the deviation from setpoint. Stability of a control loop is based on the overall transfer function between setpoint and process variable. You can have transportation lag, process dead time (use dead-time compensation). The process may not have any dead time, but control action (valve, heater, etc.) may be the source. A knowledge of each element in the loop is needed. PID may not even be the best approach. If you have a process with dead time, then you want to compensate for this in the control action.
Here again, a poorly designed process will be hard to control. I bet that if the questioner drew out the block diagram, the question would be easier to solve or even test by simulating the overall process by simple differential equations.
Control valves are the most confusing. They can have dead time, different reaction curves. The step response of the measured process variable to the control action with the loop opened (no PID at all) is needed. I would just start with the process flow sheet and instrument diagram and any trended data to help draw the loop and then build the equations from that.
Another way of answering this question is to write out the PID algorithm, and then it is seen that the terms in relation involve the "error term" three times: CO is proportional to the error, the integral of the error and the derivative of the error. Thus the time dynamics of the error term determine the stability independently of the tuning and dictate the tuning.