Imperfect data can lead to frustration, inaccurate models, poor tuning, and inconsistent control. Challenges include noisy signals, infrequent sampling, sensor drift, disturbances, and missing data. This webinar demonstrates effective techniques for working with imperfect data to develop accurate models and achieve optimal tuning results. Learn how to extract meaningful insights from inconsistent datasets, apply robust tuning methods, and improve control in real-world operating conditions.

Designed for control technicians and engineers, this session provides practical guidance on handling imperfect data effectively. Discover methods to automatically refine model accuracy and achieve stable, responsive control. Gain the skills needed to confidently tune dynamic processes in the real world, even when working with less-than-ideal data.