In addition to in-person workshops on June 6 & 7, ACC 2022 will offer some of workshops in a virtual format approximately 2 weeks before the conference, on Sunday, May 22.
Conference registrants can sign up for the workshops (both in-person and virtual) directly through the registration site. For additional information about Workshops, please contact Workshops Chair, Danny Abramovitch ([email protected]). Please note that workshops are subject to cancellation at the discretion of the organizers due to lack of registrants. Organizers and the chair will do their best to inform workshop registrants of cancellations, but please check the Workshops page at TBD often for current status.
Workshops are primarily planned to be in-person, but the list below of workshop organizers will be making an additional virtual version of their workshop available for folks who cannot attend the conference in person. This is the first time we are trying this, but we are trying to hold on to some of the outreach made available from our need to be virtual in the past two years. The content of the virtual versions of any workshop should be mostly the same as the in-person versions, notwithstanding any adjustments needed to accommodate the format change.
Below is a tentative schedule of the available virtual workshops, all scheduled for Sunday, May 22, 2022. All times are listed in Eastern Daylight Time (EDT), UTC -4. Unless otherwise noted, full-day workshops will run from 8:30am-5:30pm, EDT.
Workshops by Schedule
Full-Day Workshops (Sunday)
Organizers: Daniel Abramovitch (Agilent Technologies), Sean Andersson (Boston University), and Craig Buhr (Mathworks)
A question one should ask of any advanced algorithm is, “How do we make that work in a real system?” A question one should ask of any industrial control system is, “How do we apply better algorithms to this problem?” The two questions are dual sides of the same “bridging the gap” problem that has hounded control for decades. This workshop will examine practical methods that address this problem from both sides: ways to implement advanced algorithms on real systems and ways to improve industrial control using advanced methods. We will examine which system identification methods work on which physical systems, as model-based control requires a model. We will discuss why so many industrial controllers are PIDs, present a universal framework for different PID implementations, describe how to tune the PID to the identified system model, and show how to augment these with higher order controller dynamics (a.k.a. filters). We will discuss how to make state-space models more useable in real-time systems. Speaking of which, we will explain how to program filters and PIDs in real-time control systems. We will discuss things to know about hardware implementation and tradeoffs with ADCs, DACs, and analog filters. We will talk about the current set of real-time processing chips and the programming models that go along with them. Throughout we will offer hardware/software demonstrations of how tools like Matlab and Simulink can be used in these contexts. We won’t bridge the gap in a day, but we can move the needle.
A web page that holds the information from the brochure can be found here.
A PDF version of the workshop flyer can be found here.
Prerequisites skills for participants: Undergraduate level knowledge of feedback systems, sampled data systems, and programming. An honest interest in being able to translate control theory into physical control systems. The workshop is designed to be useful to industry practitioners wishing to apply more advanced control methods as well as academics wishing to make their algorithms more applicable to real world problems.
Organizers: Jason B. Siegel*, Anna G. Stefanopoulou, Scott Moura, and Xinfan Lin
Overview: Lithium-ion batteries have become economically viable solutions for large-scale energy storage to support renewables and are quickly reaching cost parity with internal combustion engines for transportation. There will be an increasing need for engineers that understand battery systems to design safety and control systems, develop algorithms that perform diagnostics/prognostics, and frameworks to optimize these systems for novel applications. The aim of this workshop is to introduce the methods, algorithms, and models used in developing Battery Management Systems (BMS).
Expected outcomes: At the end of the workshop participants will be able to: 1) Develop models of lithium-ion batteries from experimental data, 2) Understand the key performance metrics of battery systems, 3) Describe the degradation mechanisms responsible for loss in battery capacity and power capability 4) Develop control algorithms which safely operate the battery system and manage degradation while optimizing the performance of the system 5) identify faulty battery cells based on measurements of the current, voltage, and temperature during operation.
Intended audience: The workshop material is intended for engineers with a background in control theory who are interested to learn about batteries, but only requires a basic knowledge of chemistry and physics.
A web page that holds the information from the brochure can be found here.
