Dr. Danny Abramovitch of Agilent Technologies

Being fresh out of grad school (FOOGS) in 1988, I reacted to PID controllers in the usual way for young Ph.D. recipients: with a certain “hands-off” smugness.  Yet, as the years in industry wore on, I kept encountering these ubiquitous devices in practice and had to learn to deal with them, understand them, and roll my own.  What I found led me to consider PIDs, not as a separate art from advanced methods, but as a fundamental building block which can be used as part of most controllers, simple or advanced.  It seemed that – at least in the mechatronics world – PIDs were considered too simple for much interest in academia while practicing engineers didn’t seem to care why they were working.  In the process world, where PIDs studied far more, the issues and methods of mechatronic PIDs seemed like obscure corner cases.  Depending upon the teaching text, issues of sampling and digital representation may have been completely omitted.  There were other surprises.  While PIDs were almost universal and standard, they were almost never unified or standardized.   Furthermore, what seemed to limit performance was not the structure of the controller itself, but the lack of accurate system/process models based on repeated physical system measurements. Finally, the mechatronic and process PID goals and foibles were not that different once one considered the different system, time constant, and measurement constraints.  We will discuss these issues with the goal of getting a more unified view of PIDs across our application domains. We will provide a handful of common PID forms and show how they are related, so that we can approach any PID structure with the same analytical approach. We will finally look forward to how PIDs can be used, not only as a fundamental teaching tool for explaining control outside of our research circles, but as a critical component for advanced control methods.  As the great Paul Simon might say, “Mama don’t take my PID away.”

Biography:

Bio: Danny Abramovitch earned degrees in Electrical Engineering from Clemson (BS) and Stanford (MS and Ph.D.), doing his doctoral work under the direction of Gene Franklin. He has spent most of his career at Hewlett- Packard Labs and Agilent Labs, moving to Agilent’s Mass Spectrometry Division in 2014 to work on improved real-time computational architectures for mass spectrometers. Danny is a Fellow of the IEEE and has held leadership positions at multiple American Control Conferences, including serving as Program Chair in 2013 and General Chair in 2016. Since then, he has led outreach efforts from the controls field including a highly popular set of “practical methods” workshops. He is the holder of over 25 patents and 65 reviewed technical papers. For these efforts, he recently received the American Automatic Control Council’s inaugural Babatunde A. Ogunnaike Control Practice Award, recognizing significant contributions to the advancement of control practice (formerly known as the Control Engineering Practice Award).
Danny has spent his years in industrial research working with mechatronic control problems (optical and hard disks, atomic force microscopes) and instrumentation systems, from Agilent’s award winning first 40bps BERT to the award winning Ultivo Tandem Quad Mass Spectrometer. A consistent theme has been the need to modernize the connectivity between test benches, instrumentation, and CAD software. The need to have personally connected the pieces “from the physics to the web page” has given him a highly utilitarian view of the foundational work that needs to be done to make physical systems truly data driven. Over the past decade he has focused much of his effort on how to teach the principles, limitations, and requirements of feedback systems to people outside the traditional controls community including high school and college STEM students, scientists and practicing engineers, as well as the general public.

Address:United States