Although the industrial automation industry has, for the most part, adopted the proportional-integral-derivative (PID) algorithm as the de facto standard for closed-loop process control, the best means of tuning a PID loop to achieve optimal performance still is an open question. The exercise is conceptually simple: Choose the gain, rate, and reset parameters that define the relative magnitude of the P, I, and D contributions to the overall control effort.
In practice, loop tuning often is more of an art than a science. The best choice of tuning parameters depends on a variety of factors including the dynamic behavior of the controlled process, the performance objectives specified by the operator, and the operator’s understanding of how tuning works. A variety of manual techniques have been developed to help operators tune their loops, but even with the aid of loop-tuning software, loop tuning can be a difficult and time-consuming chore. See “Loop Tuning Fundamentals,” Control Engineering, July 2003.
“Autotuning” or “self-tuning” PID controllers are designed to simplify matters by choosing their own tuning parameters based on some sort of automated analysis of the controlled process’s behavior. These automatic procedures often involve a mathematical model of the process’s input/output relationship derived from process data augmented by information provided by an experienced operator (see Figure 1). “Self-tuning” refers to such procedures continuously executed while the controller is online regulating the process. “Autotuning” refers to on demand procedures executed while the controller is offline.
However, the two terms often are used interchangeably because both self-tuning and autotuning controllers automatically tune themselves. For simplicity’s sake, both will be described as “autotuners” hereafter.