Theoretically, all information that a feedback controller requires to regulate a continuous process is contained in the process input and output (I/O) data. A PID controller can be manually tuned by analyzing the I/O data from a series of step tests. A self-tuning controller can automatically select its own tuning parameters by analyzing a process model derived from the step test data.
In your recent "How Software Tools Simplify Loop Tuning" article [Control Engineering, Nov. 1997, p. 89] you imply that ExperTune connects only to PLCs. ExperTune has a DDE connection that connects directly to Fisher/Provox (via SIU, HDL), Honeywell TDC systems (using GUS workstations), and Bailey DCS (via SIU).
Marlborough, Mass.— Dataflo-C is a digital proportional, integral, derivative single-loop controller. The device, which is aimed at industrial processes, features a rugged valve and actuator package, on-board diagnostics, autotuning capability, and remote or local setpoint control. It accepts a variety of process inputs and can take total control of process parameters either locally or v...
One of the advantages that programmable logic controllers (PLCs) have traditionally offered for the purposes of real-time control has been deterministic operations. That is, they have the ability to apply their control efforts at precisely timed intervals. This feature allows a PLC to work with sampled data, knowing that each sample represents a uniform period of process activity.
Tuning a PID controller is conceptually simple–observe the behavior of the controlled process and fine tune the controller’s proportional (P), integral (I), and derivative (D) parameters until the closed-loop system performs as desired. However, PID tuning is often more of an art than a science. The best choice of tuning parameters depends upon a variety of factors including the dynamic behavior of the controlled process, the controller’s objectives, and the operator’s understanding of the tuning procedures. Self-tuning PID controllers simplify matters by executing the necessary tuning procedures automatically.
A feedback controller is designed to generate an output that causes some corrective effort to be applied to a process so as to drive a measurable process variable towards a desired value known as the setpoint. Shown is a typical feedback control loop with blocks representing the dynamic elements of the system and arrows representing the flow of information, generally in the form of electrical signals. Virtually all feedback controllers determine their output by observing the error between the setpoint and the actual process variable measurement. PID control A proportional-integral-derivative or ‘PID’ controller looks at the current value of the error, the integral of the error over a recent time interval, and the current derivative of the error signal to determine not only how much of a correction to apply, but for how long.
Controllers that juggle multiple process variables are neither simple nor common, but they can handle some of the most complex control problems.
Arguably the trickiest problem to overcome with a feedback controller is process deadtime -- the delay between the application of a control effort and its first effect on the process variable. During that interval, the process does not respond to the controller's activity at all, and any attempt to manipulate the process variable before the deadtime has elapsed inevitably fails. This classic article is among the most-read on the Control Engineering site. (See diagrams.)