Closing Ceremony on Wednesday August 26 from 18:15-19:15 Montreal Time (visit SOCIAL EVENTS page for Zoom link)

Due to the ongoing pandemic, the conference will be held online. Please see the reduced registration fees along with a new registration category for non-author IEEE CSS members in the registration page. IEEE CSS support will be available for a limited number of students for conference registration.

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Welcome letter from the Prime Minister’s office

General Chair’s Welcome Note:

As the Chair of the 4th IEEE Conference on Control Technology and Applications (CCTA 2020), I would like to invite you to join us, virtually, in this great event. The planning of this event began three years ago, and we were aiming to hold it as an in-person conference. Only a few months before the conference we decided to change the plan due to the COVID-19 pandemic, but we are still confident that the attendees will have a one-of-a-kind experience. We certainly hope that Montreal, with its signature blend of North American and European cultures, will be one of your target places to visit after the pandemic is over.  After all, there is no better way to complete your conference experience by adding a tremendous non-virtual feeling to it.  

The CCTA is one of the conferences sponsored by the IEEE Control Systems Society and replaces the successful former IEEE CCA and IEEE MSC. The CCTA 2020 Technical Program will include regular and invited sessions, as well as application-oriented tutorial and workshop sessions, and of course, some Plenary Lectures on the cutting-edge control technology topics.

We hope you will join us for a productive CCTA in an elegant online format. You will remember this event, not for its virtual setting but its real experience. See you (online) in August 2020!

Amir G. Aghdam
Professor, Electrical and Computer Engineering
Concordia University

Plenary Talks

Somayeh Sojoudi
Assistant Professor at UC Berkeley

Title: Efficient Computational Methods with Provable Guarantees for Data-Driven Problems

Abstract: The area of data science lacks efficient computational methods with provable guarantees that can cope with the large-scale nature and the high nonlinearity of many real-world systems. Practitioners often design heuristic algorithms tailored to specific applications, but the theoretical underpinnings of these methods remain a mystery and this limits their usage in safety-critical systems. In this talk, we investigate the above issue for some canonical data-driven problems with connections to optimization and control theory. First, we study the problem of learning a statistical model or a physical model of a real-world system from data.  On the statistical learning side, we consider the graphical Lasso (GL) that is a popular optimization method for learning graphical models from data. By analyzing the properties of this conic problem, we show that its true computational complexity is indeed linear for sparse graphical models, which enables designing new algorithms for this problem to be able to solve large-scale learning problems efficiently.  We then study the problem of learning the physical model of an unknown sparse dynamical system, for which we derive sharp bounds on the amount of data required to reliably identify the system or design an optimal control policy. We offer case studies in neuroscience, transportation and other networks with as many as 200,000 parameters.

Second, we study the problem of solving nonlinear optimization problems efficiently using low-complexity methods. Nonlinearity is ubiquitous in control theory, and more recently has played a major role in deep learning and artificial intelligence. We discuss the recent advances in this area. In particular, we introduce the notion of “global functions”, as a major generalization of convex functions, which allows us to study the non-existence of spurious local minima for nonconvex and nonsmooth learning problems and enables solving such problems using stochastic gradient descent. We demonstrate the results on tensor decomposition with outliers, video processing, and online optimization in machine learning. 

Biography: Somayeh Sojoudi is an Assistant Professor in the Departments of Electrical Engineering & Computer Sciences and Mechanical Engineering at the University of California, Berkeley. She is also on the faculty of the Tsinghua-Berkeley Shenzhen Institute (TBSI). She received her Ph.D. degree in Control & Dynamical Systems from the California Institute of Technology in 2013. She has been working on several interdisciplinary problems in optimization theory, control theory, machine learning, and power systems. Somayeh Sojoudi is an Associate Editor for the journals of the IEEE Transactions on Smart Grid, IEEE Access, and Systems & Control Letters. She is also a member of the conference editorial board of the IEEE Control Systems Society. She has received the 2015 INFORMS Optimization Society Prize for Young Researchers and the 2016 INFORMS Energy Best Publication Award. She has been a finalist (as an advisor) for the Best Student Paper Award of the 2018 American Control Conference and a finalist (as a co-author) for the Best Student Paper Award of the 53rd IEEE Conference on Decision and Control. Her paper on graphical models has received the INFORMS 2018 Data Mining Best Paper Award. 

Daniel Abramovitch
System Architect at Agilent Technologies, Inc.

Title: Bridging the Gap: Using Real-World Problems to Unveil Deep Control Principles

Abstract: This talk is about the deep principles of control and system theory that one can uncover by working on practical control applications and physical, real-world problems. I want to convince you of something that you want to believe: that control and system theory are vital to the world at large and to the soon to be ubiquitous technologies of automated devices, vehicles, instruments, appliances, and our future robotic helpers.  I also need to convince you of something most of us don’t want to believe: that we, the controls community, would vastly increase our influence on the world by refocusing our work as a framework that helps us build real-world systems rather than simply a mathematical exercise.  We need to understand that for us to be the control engineers that the world needs, we must dramatically increase our outreach to practicing engineers and the general public. 

To make a case for how to do this, I will present examples of industrial problems that have led not only to new products, but to new understanding about deep control principles that were not readily apparent from the theoretical side of the gap.   Such insights build bridges for us to add advanced methods to a much larger group of real-world control problems. Conversely, they also drive physical motivation and context for our theory.  Not only do such problems allow us to improve the results of practicing engineers, but they allow us to familiarize the wider public with the fundamental principles of control and system theory and the underlying role that they play in many familiar technologies. The world continues to see dramatic increases in devices and services that are fundamentally dependent upon the principles of control and system theory. If we, the controls research community, want to take a leadership role in this technology globalization, we have some work to do.

Biography: Dr. Abramovitch earned degrees in Electrical Engineering from Clemson (BS) and Stanford (MS and Ph.D.). After a brief stay at Ford Aerospace, he worked at HP Labs for 11 1/2 years, studying control issues for optical and magnetic disk drives.  He moved to Agilent Labs shortly after the spinoff from HP, where he has spent 20 years working on test and measurement systems.  He is currently in Agilent’s Mass Spectrometry Division working on improved real-time computational architectures for Agilent’s mass spectrometers.  Danny is a Senior Member of the IEEE and was Vice-Chair for Industry and Applications for the 2004 American Control Conference (ACC), for Workshops at the 2006 ACC, for Special Sessions at the 2007 ACC, and for Industry and Applications for the 2009 ACC. He was Program Chair for the 2013 ACC and General Chair of the 2016 ACC in Boston. He has organized tutorial sessions on disk drives, atomic force microscopes, phase-locked loops, laser interferometry, and how business models and mechanics affect control design. He was Chair of the IEEE CSS History Committee from 2001 to 2010.  Danny had the original idea for the clocking mechanism behind the DVD+RW optical disk format.  He was on the team that prototyped Agilent’s first 40Gbps Bit Error Rate Tester (BERT).  He and Gene Franklin were awarded the 2003 IEEE Control Systems Magazine Outstanding Paper Award. He was a Keynote Lecturer at the 2015 MSC in Sydney.  His recent work for Agilent was on high-speed atomic force microscopes and high precision interferometers, and currently works on improving the real-time control, data collection, and signal processing chain on Agilent’s Mass Spectrometers.  He is part of the team that introduced the multi-award-winning Ultivo Tandem Quad Mass Spec in 2017. He is the holder of over 20 patents and has published over 50 reviewed technical papers.



CSS Transition to Practice Award Lecture:

Alberto Bemporad
Professor at IMT School for Advanced Studies

Title: Model Predictive Control: A Rising Technology in the Automotive Industry

Abstract: Model Predictive Control (MPC) is widely recognized as a very effective advanced control technique for multivariable systems subject to constraints on input and output variables. At each sampling step, MPC selects a sequence of manipulated inputs that, according to predictions made by a dynamical model of the controlled process, optimizes a certain performance index under constraints. The main challenges in applying MPC to real industrial problems are (i) how to get suitable prediction models, especially directly from experimental data, (ii) how to solve the optimization problem associated with MPC online with high throughput, limited machine precision, limited memory resources, and within hard real-time constraints, and (iii) how to help industrial calibrators tuning the design knobs of the controller. In my talk, I will describe the main concepts of MPC and present some recent developments in embedded optimization for MPC and in the calibration of MPC from data. I will also highlight the main advantages of MPC over more classical methods, in terms of better multi-actuator coordination, reduced setup time and calibration effort, and easier maintenance and portability of the design. Finally, I will provide evidence of how MPC has now become a viable technology for production, thanks to the availability of specialized design and real-time software, giving concrete examples of MPC controllers already in mass production in the automotive industry.

Biography: Alberto Bemporad received his Master’s degree in Electrical Engineering in 1993 and his Ph.D. in Control Engineering in 1997 from the University of Florence, Italy. In 1996/97 he was with the Center for Robotics and Automation, Department of Systems Science & Mathematics, Washington University, St. Louis. In 1997-1999 he held a postdoctoral position at the Automatic Control Laboratory, ETH Zurich, Switzerland, where he collaborated as a senior researcher until 2002. In 1999-2009 he was with the Department of Information Engineering of the University of Siena, Italy, becoming an Associate Professor in 2005. In 2010-2011 he was with the Department of Mechanical and Structural Engineering of the University of Trento, Italy. Since 2011 he is Full Professor at the IMT School for Advanced Studies Lucca, Italy, where he served as the Director of the institute in 2012-2015. He spent visiting periods at Stanford University, University of Michigan, and Zhejiang University. In 2011 he cofounded ODYS S.r.l., a company specialized in developing model predictive control systems for industrial production. He has published more than 350 papers in the areas of model predictive control, hybrid systems, optimization, automotive control, and is the co-inventor of 16 patents. He is author or coauthor of various MATLAB toolboxes for model predictive control design, including the Model Predictive Control Toolbox (The Mathworks, Inc.), the Hybrid Toolbox, the MPCTool and MPCSofT toolboxes developed for the European Space Agency, and other MPC software packages tailored to industrial production. He was an Associate Editor of the IEEE Transactions on Automatic Control during 2001-2004 and Chair of the Technical Committee on Hybrid Systems of the IEEE Control Systems Society in 2002-2010. He received the IFAC High-Impact Paper Award for the 2011-14 triennial and the IEEE CSS Transition to Practice Award in 2019. He is an IEEE Fellow since 2010. WEB PAGE: http://imt.lu/ab