ABSTRACT
Model predictive control has become a pervasive advanced control technology in which optimal control of a multivariable system with input and state constraints is combined with a moving horizon to produce a feedback controller. In applications, model predictive control is often used to solve constrained tracking problems. The tracking problem arises in some settings as the basic goal of the control system, and the constraint handling capabilities of MPC are what make it attractive. In other applications, however, there may be a higher-level goal, such as economic optimization of a process, and this goal is first translated into a steady-state tracking problem. Since MPC enables the designer to choose the objective function that is optimized online, it offers the potential to treat the higher-level control goal directly within the MPC controller bypassing this translation into a steady-state setpoint and tracking problem. In this talk we explore the possibilities enabled by MPC to address these types of high-level goals. We also outline some of the open research challenges presented by this approach; these include modeling, optimization, and controller design challenges. The talk concludes with a brief presentation of a recently deployed economic optimization technology developed by Johnson Controls to control the campus energy system at Stanford University.

BIOGRAPHY
James B. Rawlings received the B.S. from the University of Texas and the Ph.D. from the University of Wisconsin, both in Chemical Engineering. He spent one year at the University of Stuttgart as a NATO postdoctoral fellow and then joined the faculty at the University of Texas. He moved to the University of Wisconsin in 1995 and is currently the Steenbock Professor of Engineering and W. Harmon Ray Professor of Chemical and Biological Engineering, and the co-director of the Texas-Wisconsin-California Control Consortium (TWCCC). Professor Rawlings's research interests are in the areas of chemical process modeling, monitoring and control, nonlinear model predictive control, moving horizon state estimation, and molecular-scale chemical reaction engineering. He has written numerous research articles and coauthored three textbooks: "Modeling and Analysis Principles for Chemical and Biological Engineers" (2013) with Mike Graham, "Model Predictive Control: Theory and Design" (2009), with David Mayne, and "Chemical Reactor Analysis and Design Fundamentals," 2nd ed. (2012), with John Ekerdt. In recognition of his research and teaching, Professor Rawlings has received several awards including: National Academy of Engineering; "Doctor technices honoris causa" from the Danish Technical University; The inaugural High Impact Paper Award from the International Federation of Automatic Control; The Ragazzini Education Award from the American Automatic Control Council; The Computing in Chemical Engineering Award and Excellence in Process Development Award from the AICHE; The Chancellor's Distinguished Teaching Award, a WARF Named Professorship, and the Byron Bird Award for Excellence in a Research Publication, from the University of Wisconsin; He is a fellow of IFAC, IEEE, and AIChE.

A Data-driven Approach to Nonlinear Systems Control, Robotics, and Life Sciences in the Era of Big Data
Professor Harry Asada
Massachusetts Institute of Technology (MIT), USA

ABSTRACT
Data-driven techniques are increasingly important as a vast amount of data is becoming available at low cost. Yet, effective methodologies are still lacking for extracting critical information needed for control design from large data sets. This talk will address how we can exploit Big Data, both measured and simulated, for complex system modeling and control. First, a new data-driven approach to building an effective state equation will be developed by using Latent Variable methods combined with physical modeling theory, the Bond Graph. Two major features and challenges will be addressed. One is to find a complete set of variables that can sufficiently inform the system’s nonlinear dynamics. Independent state variables are augmented by adding auxiliary variables that are needed for describing constitutive laws of individual components, which may be nonlinear. The other is to show that a class of nonlinear dynamical systems behaves linearly when it is recast in a high-dimensional space derived from the augmented state space that is sufficiently informative. While the resultant latent state equation is linear, complex nonlinearities are embedded in the compact model, leading to precise and global linearization of nonlinear dynamics. The new methodology will be applied to: a) Model Predictive Control of nonlinear systems, b) global stable control of underwater robots subject to nonlinear hydrodynamics, including ground effect and nonlinear drag, and c) intracellular and intercellular biochemical dynamics of interacting cells. These examples demonstrate that linearity of the augmented state equations allows for a) eliminating complex nonlinear stochastic prediction, b) supernumerary state feedback for assuring global stability, and c) superposition of multiple solutions to predict emergent behaviors of interacting nonlinear agents, which would otherwise be prohibitively complex to compute.

BIOGRAPHY
H. Harry Asada is Ford Professor of Engineering and Director of the Brit and Alex d’Arbeloff Laboratory for Information Systems and Technology in the Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA. He received his B.S., M.S., and Ph.D. degrees in precision engineering in 1973, 1975, and 1979, respectively, all from Kyoto University, Japan. He specializes in robotics, biological engineering, and system dynamics and control. His current research includes precise, global linearization of nonlinear systems, wearable extra legs, arms, and fingers, underwater robots, bio-artificial muscles, optogenetic control, computational modeling of cell migration and emergent behaviors. He received the Rufus Oldenburger Medal from ASME in 2011. He won the Best Paper Awards at the IEEE International Conference on Robotics and Automation in 1993, 1997, 1999, and 2010, the O. Hugo Schuck Best Paper Award from the American Control Council in 1985, Best Journal Paper Awards from the Society of Instrument and Control Engineers in 1979, 1984, and 1990, and the Henry Paynter Outstanding Researcher Award from ASME Dynamic Systems and Control in 1998. He also received the Spira Award for Distinguished Teaching from the School of Engineering, MIT. Dr. Asada is a Fellow of ASME.

Graph-Theoretic Convexification of Polynomial Optimization Problems with Applications to Power Systems and Distributed Control
Professor Javad Lavaei (2016 Eckman Award Winner)
University of California, Berkeley, USA

ABSTRACT
The area of polynomial optimization has been actively studied in computer science, operations research, applied mathematics and engineering, where the goal is to find a high-quality solution using an efficient computational method. This area has attracted much attention in the control community since several long-standing control problems could be converted to polynomial optimization problems. The current researches on this area have been mostly focused on various important questions: i) how does the underlying structure of an optimization problem affect its complexity? Ii) how does sparsity help? iii) how to find a near globally optimal solution whenever it is hard to find a global minimum? iv) how to design an efficient numerical algorithm for large-scale non-convex optimization problems? v) how to deal with problems with a mix of continuous and discrete variables? In this talk, we will develop a unified mathematical framework to study the above problems. Our framework rests on recent advances in graph theory and optimization, including the notions of OS-vertex sequence and treewidth, matrix completion, semidefinite programming, and low-rank optimization. We will also apply our results to two areas of power systems and distributed control. In particular, we will discuss how our results could be used to address several hard problems for power systems such as optimal power flow (OPF), security-constrained OPF, state estimation, and unit commitment.

BIOGRAPHY
Javad Lavaei is an Assistant Professor in the Department of Industrial Engineering and Operations Research at University of California, Berkeley. He was an Assistant Professor in Electrical Engineering at Columbia University from 2012 to 2015. He received the Ph.D. degree in Control & Dynamical Systems from the California Institute of Technology in 2011, and was a postdoctoral scholar in Electrical Engineering and Precourt Institute for Energy at Stanford University for one year. He is the recipient of the Milton and Francis Clauser Doctoral Prize for the best university-wide Ph.D. thesis, entitled "Large-Scale Complex Systems: From Antenna Circuits to Power Grids". He researches on optimization theory, control theory and power systems. He has won several awards, including DARPA Young Faculty Award, Office of Naval Research Young Investigator Award, National Science Foundation CAREER Award, Resonate Award, Google Faculty Research Award, Governor General of Canada Academic Gold Medal, Northeastern Association of Graduate Schools Master's Thesis Award, and Silver Medal in the 1999 International Mathematical Olympiad. Javad Lavaei is an associate editor of IEEE Transactions on Smart Grid and serves on the conference editorial board of IEEE Control Systems Society and European Control Association. He was a finalist (as an advisor) for the Best Student Paper Award at the 53rd IEEE Conference on Decision and Control 2014. His journal paper entitled "Zero Duality Gap in Optimal Power Flow Problem" has received a prize paper award given by the IEEE PES Power System Analysis Computing and Economics Committee in 2015. He is a co-recipient of the 2015 INFORMS Optimization Society Prize for Young Researchers, and the recipient of the 2016 Donald P. Eckman Award given by the American Automatic Control Council
 

Model Reduction of Networks Preserving the Network First and Second Order Structure
Professor Jacquelien Scherpen
University of Groningen, The Netherlands

ABSTRACT
Network systems have received a lot of attention in the past decade. They are used to analyze and design communication network, smart grid technology, social media, social dynamics, formation and consensus problems, etc. Several analysis and control methods have been developed for network systems. However, often, their large scale nature makes it difficult to analyze and to design a controller. We develop methods to reduce the order of the network while preserving the network structure, as well as some structure of the (linear) node dynamics. In particular, second order network dynamics structure is preserved. We use node clustering methods, as well as a state space singular value decomposition based method. For the first we provide error bounds. We illustrate the results with help of some relevant high order examples.

BIOGRAPHY
Jacquelien M. A. Scherpen received the M.Sc. and Ph.D. degrees in applied mathematics from the University of Twente, Enschede, The Netherlands, in 1990 and 1994, respectively. She was with Delft University of Technology, The Netherlands, from 1994 to 2006. Since September 2006, she is a professor at the University of Groningen, at the Engineering and Technology institute Groningen (ENTEG) of the Faculty of Mathematics and Natural Sciences, The Netherlands. Since 2013 she is the scientific director of ENTEG. She is a member of the Jan C. Willems Center for Systems and Control of the University of Groningen, and board member of the Dutch Institute of Systems and Control. She has held visiting research positions at the University of Tokyo, and Kyoto University, Japan, Université de Compiegne, and SUPELEC, Gif-sur-Yvette, France, and the Old Dominion University, Norfolk, VA, USA. She has been an Associate Editor of the IEEE Transactions on Automatic Control, the International Journal of Robust and Nonlinear Control (IJRNC) and the IMA Journal of Mathematical Control and Information. She is on the editorial board of the IJRNC. Her current research interests include (linear and nonlinear) model reduction methods, nonlinear control methods, modeling and control of physical systems using the concepts of passivity and dissipativity, and distributed optimal control applications for smart energy system.