The ACC is internationally recognized as a premier scientific and engineering conference dedicated to the advancement of control theory and practice. The ACC brings together an international community of researchers and practitioners to discuss the latest findings in control research and practice. The 2014 ACC will feature several kinds of presentations including contributed and invited papers, invited sessions, tutorial sessions, and special sessions along with workshops and exhibits.
For more information visit http://a2c2.org/conferences/acc2014/index.php
Workshop: Developing Battery Management Systems Using Model-Based Design
Presenters: Simona Onori (Clemson University), Federico Baronti (University of Pisa), Mo-Yuen Chow (North Carolina State University), Robyn Jackey (MathWorks), Kevin Rzemien (MathWorks)
Date: Tuesday, June 3 – ½ day
In this workshop, we will provide an overview of the current state of the art, technical and technological challenges, and future research directions for battery management system (BMS) design in electrified vehicles.
Specifically, the workshop will provide a technology overview, including the latest techniques in battery systems modeling, control, and diagnosis, along with current trends in modeling energy storage systems for automotive applications. We will then introduce methods for battery models using experimental data with emphasis on parameter identification techniques for both battery cell and battery pack modeling design. Next, we discuss state of charge and state of health estimation methods, including analysis and comparison of different model-based approaches using field application data.
The development of safe management systems is critical for the Li-ion battery industry. In the second part of the workshop, model-based design techniques will be used to design, implement, and validate algorithms that ensure the safety of a Li-ion battery pack by monitoring each individual cell for overvoltage or undervoltage conditions. Next, the discussion will move to charge equalization to discuss open issues and compare different solutions. Finally, some BMS implementation examples will be presented.
Workshop: 40 Years of Robust Control: 1978 to 2018
Presenters: Gary Balas (University of Minnesota), John Doyle (Caltech), Pascal Gahinet (The MathWorks), Keith Glover (Cambridge), Andy Packard (UC Berkeley), Pete Seiler (University of Minnesota), Roy Smith (ETHZ)
Date: Tuesday, June 3 – full day
This full-day workshop is mostly of a tutorial nature. The basic paradigm for robust control was formulated in the late 70’s and early 80’s, starting with interesting examples illustrating non-intuitive robustness properties of multi-loop systems. Over a 10-year period, research flourished, understanding these issues, developing rigorous analysis techniques based on efficient computations, and devising approximate methods for controller synthesis. These tools are used ubiquitously throughout various industries. Active research continues to this day, with generalizations along several diverse directions, including distributed and decentralized systems, nonlinear systems and novel applications. The purpose of this 1-day workshop is to present a concise, tutorial summary of the initial research phase, and present the results of more recent research activity. Participants are encouraged to bring laptops and work through the tutorial exercises during the workshop. One-day licenses of Matlab will be available for installation.
Sponsored Session: System modeling, control design, and low-cost hardware implementation of mechatronic systems
Presenter: Craig Buhr, MathWorks
Date: Wednesday, June 4th
Low-cost hardware solutions are used in many applications such as quadcopters and mobile robots. These low-cost solutions provide an effective platform to give students hand-on experience through project based learning and competitions. In this presentation, we will demonstrate each step of the process for developing an embedded control system for a mobile robot using the LEGO MINDSTORM hardware. We show how to model and analyze the robot through simulation as well as design and validate a feedback controller using the simulated model. Finally we deploy the control algorithm to the robot and validate its performance.
3 Jun 2014