CCDC 2020
23-25 May


Control and Optimization for Integration of Renewables in Smart Grids.

Prof. Anuradha Annaswamy

Massachusetts Institute of Technology, USA


World over, there’s a big push towards a 100% incorporation of wind and solar power for electricity production. Significant changes have occurred over the past decade in the energy landscape, especially in the power sector. Natural gas prices have declined, costs of renewable energy technologies have come down, and large‐scale battery energy storage technologies have advanced rapidly. There are however a host of challenges, most of which are due to the intermittency and unpredictability of the renewable energy resources. Most of the requisite solutions for the deep integration of these renewable resources for electricity production are control‐centric. A distributed optimization approach that judiciously combines renewable generation with storage and flexible loads has the possibility for ensuring power balances. A distributed control approach that enables a coordinated network of millions of controllers, all integrated with solar and wind power generation nodes, storage sites, and flexible consumption can lead to effective frequency regulation and voltage control in real‐time. In this talk, some of these challenges, highlights of the current research in distributed optimization and control, and opportunities for future directions will be discussed. Examples of use cases that illustrate the role of optimization and control in renewable‐rich power grids will be presented.


Dr. Anuradha Annaswamy is Founder and Director of the Active-Adaptive Control Laboratory in the Department of Mechanical Engineering at MIT. Her research interests span adaptive control theory and its applications to aerospace, automotive, and propulsion systems as well as cyber physical systems such as Smart Grids, Smart Cities, and Smart Infrastructures. Her current research team of 15 students and post-docs is supported at present by the US Air-Force Research Laboratory, US Department of Energy, Boeing, Ford-MIT Alliance, and NSF. She has received best paper awards (Axelby; CSM), Distinguished Member and Distinguished Lecturer awards from IEEE CSS, and a PYI award from NSF. She is a Fellow of IEEE and IFAC. She will serve as the CSS President in 2020.

Digital retina: a system framework to improve cloud vision computing to brain like vision computing.

Prof. Wen Gao

Beijing Univ., China


The wave of smart city makes most of the computing power of urban cloud computing system consumed by image and video retrieval and analysis, and with the popularization of application, the demand for computing power is growing, and the investment is also growing. In order to alleviate this contradiction, more and more video devices in urban cloud vision system are upgraded from traditional cameras to intelligent terminals or intelligent edge devices. However, there are still different arguments about how much intelligence terminals and edge devices should have, and how cloud computing systems balance the consistency and intelligence of systems. Human vision system (HVS) has experienced hundreds of millions of years of evolution to its current state. It may not be perfect in some senses, but it is much better than any existing computer vision system, whether based on cloud computing or supercomputer system. Most of the artificial vision system is composed of camera and computer, which is equivalent to eyes and brain for human. But compared with human beings, the development level of visual pathway model between them is very low, almost a simple one-way communication link. The pathway model between the human eye and brain is quite complex, it is highly efficient and globally accurate. It is evolved from natural selection. This talk will introduce a new idea to improve the cloud vision system, through the visual pathway model of human like vision system, called Digital Retina, so as to make it more efficient and intelligent. The Digital Retina framework has three key features, details of which will be given in the talk.


Prof. Wen Gao Wen Gao now is a Boya Chair Professor at Peking university. He also serves as the president of China Computer Federation (CCF) from 2016 to 2020.

He received his Ph.D. degree in electronics engineering from the University of Tokyo in 1991. He joined with Harbin Institute of Technology from 1991 to 1996, and Institute of Computing Technology (ICT), Chinese Academy of Sciences (CAS) from 1996 to 2006. He joined the Peking University since February of 2006.

Prof. Gao works in the areas of multimedia and computer vision, topics including video coding, video analysis, multimedia retrieval, face recognition, multimodal interfaces, and virtual reality. His most cited contributions are model-based video coding and feature-based object representation. He published seven books, over 300 papers in refereed journals, and over 700 papers in selected international conferences. He is a fellow of IEEE, a fellow of ACM, and a member of Chinese Academy of Engineering.

Control theory of switches and clocks.

Prof. Rodolphe Sepulchre

University of Cambridge, UK


This talk will present a novel approach for the analysis and design of systems that switch and oscillate. While such nonlinear behaviors abound in control engineering of electrical, mechanical, and biological circuits, it is often considered that they fall outside the scope of control theory. In contrast, the proposed approach closely mimics linear-quadratic dissipativity theory, a very foundation of modern control theory.

In its classical formulation, dissipativity theory formulates system properties as dissipation inequalities to be satisfied by the storage, an abstraction of the system internal energy. Linear systems admit quadratic storages. When the storage is positive definite, it serves as a Lyapunov function for stability analysis of equilibria. Our generalization rests on two distinct ingredients. First, we apply dissipativity theory differentially: instead of studying the nonlinear system via the nonlinear theory, we apply the linear theory to a family of linearized systems. Second, we relax the positivity constraint of the quadratic storage to a fixed inertia constraint. We allow for one negative eigenvalue in the analysis of switches and two negative eigenvalues in the analysis of clocks.

The talk will illustrate the theory in classical models of switches and clocks and discuss the potential of dissipativity theory for the analysis and design of interconnected systems away from equilibrium.


Rodolphe Sepulchre received the engineering degree and the Ph.D. degree from the Université catholique de Louvain in 1990 and in 1994, respectively. From 1994-1996, he was as postdoctoral research associate at the University of California, Santa Barbara. In 1997, he joined the Université de Liege, where he was professor until 2013. In 2013, he joined Cambridge University and also became a professioral fellow of Sidney Sussex College. He held visiting positions at Princeton University (2002-2003), the Ecole des Mines de Paris (2009-2010), Caltech (2018), and part-time positions at the University of Louvain (2000-2011) and at INRIA Lille Europe (2012-2013).

His research interests are in nonlinear control and optimization theory. He co-authored the monographs "Constructive Nonlinear Control" (Springer-Verlag, 1997) and "Optimization on Matrix Manifolds" (Princeton University Press, 2008). From 2009, his research has been increasingly motivated by control questions from neuroscience. A current focus is his ERC advanced grant "Switchlets", aiming at a multi-scale control theory of excitable systems.

He is currently Editor-in-Chief of the IEEE Control Systems Magazine and an Associate Editor for Annual Review in Control, Robotics, and Autonomous Systems. He was Editor-in-Chief of Systems and Control Letters from 2009-2018 and has also served as an Associate Editor for several journals, including, Transactions on Network Science and Engineering, Automatica, SIAM Journal of Control and Optimization, the Journal of Nonlinear Science, and Mathematics for Control, Signals, and Systems. In 2008, he was awarded the IEEE Control Systems Society Antonio Ruberti Young Researcher Prize. He is a fellow of IEEE, SIAM, and IFAC. He has been IEEE CSS distinguished lecturer between 2010 and 2015. In 2013, he was elected at the Royal Academy of Belgium.

Systems and Control Theory for Advanced Manufacturing.

Prof. Richard D. Braatz

Massachusetts Institute of Technology, USA


The world is seeing renewed interest in advanced manufacturing, which can be seen in a number of initiatives from governments and industry-government partnerships with such names as Industry 4.0 and Smart Manufacturing. After discussing the role of cyber-physical systems, the Internet of Things, and cloud computing, this presentation describes systems and control theories that underpin the actual processes employed in the manufacturing of high-tech products. These manufacturing processes typically have (1) high to infinite state dimension, (2) probabilistic uncertainties, (3) time delays, (4) unstable zero dynamics, (5) actuator, state, and output constraints, (6) stochastic noise and disturbances, and (7) phenomena described by combinations of algebraic, ordinary differential, partial differential, and integral equations (that is, generalizations of descriptor/singular systems). Key points are illustrated for fully automated, advanced, and modular manufacturing systems developed at MIT. Stochastic model predictive control formulations are presented that have the flexibility to handle linear dynamical systems with these characteristics, while employing projections and shifting of the most expensive calculations offline so that the online computational cost is low. Implementation to a detailed mechanistic model of an advanced drug manufacturing plant demonstrates an order-of-magnitude improved robustness of the product quality to model uncertainties while having an online optimization cost of less than 1 second. Some extensions to nonlinear dynamical systems are discussed.


Richard D. Braatz is the Edwin R. Gilliland Professor at the Massachusetts Institute of Technology (MIT) where he does research in applied mathematics and control theory and their application to advanced manufacturing systems. He received MS and PhD from the California Institute of Technology and was on the faculty at the University of Illinois at Urbana-Champaign and a Visiting Scholar at Harvard University before moving to MIT. He is a Fellow of IEEE and IFAC, and a member of the U.S. National Academy of Engineering.