While conceptually simple, step tests can be a challenge to automate. The results will be skewed if a disturbance happens to intrude on the process variable while the test is in progress. An experienced operator performing a manual step test can generally recognize a disturbance in progress and either wait to start the test or restart it as necessary. Endowing an autotuner with similar observational skills is much trickier. That problem is particularly acute when the process variable is subject to measurement noise.
An autotuner can’t always distinguish between phantom noise and real disturbances. And even when it can, the measurement noise might still corrupt the calculation of the process model by obscuring the exact shape of the reaction curve.
Some autotuners can deal with measurement noise by executing their automatic step tests more than once and then averaging the results or selecting the results that turn up most often. A sophisticated autotuner also can calculate how well its estimates of the process model fit the noisy data and either report how confident it is in its latest results or repeat the test until it reaches an operator-defined confidence level.