CCDC 2020
23-25 May

大会报告

Control and Optimization for Integration of Renewables in Smart Grids.

Anuradha Annaswamy 教授

Massachusetts Institute of Technology, 美国

Abstract

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.

Biography

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.


数字视网膜 – 从云视觉计算走向类脑视觉计算之模型框架

高文 教授

北京大学,中国

Abstract

       智能城市浪潮使得城市云计算系统绝大部分算力被图像和视频的检索与分析所消耗,而且随着应用的普及对算力的需求越来越大,资金投入也越来越大。为了缓解此矛盾,城市云视觉系统中越来越多的视频设备从传统摄像机升级为智能终端或者智能边缘设备。然而,对于终端和边缘设备到底应该具备多少智能,以及云计算系统如何平衡系统的一致性与智能性,仍有一些不同争论。人类视觉系统(HVS)经历了数亿年的进化达到其目前的状态,它可能还不完善,但比任何现有的计算机视觉系统要好得多,不论是基于云计算还是超级计算机系统。大多数的人工视觉系统是由摄像机和计算机组成的,对人类来说就相当于眼睛和大脑。但与人类相比,两者之间的视觉通路模型水平很低,几乎就是简单通信链路。人眼和大脑之间的通路模型是相当复杂的,但高能效和全局准确,它是由自然选择进化而来。本报告将介绍讨论一种新的思路,即通过类人了视觉系统的视觉通路模型(称为数字视网膜)来改进云视觉系统,使之更加高效和智能。数字视网膜框架模型有三组关键特征,细节将在报告中给出。

Biography

  高文 ,北京大学博雅讲座教授、信息科学技术学院院长、深圳鹏城实验室主任。1982年于哈科大获得学士学位,1985年于哈工大获得硕士学位,1988年和1991分别获得哈工大计算机应用博士学位和东京大学电子工程博士学位。1991至1995年就职于哈尔滨工业大学,1996至2005就职于中国科学院计算技术研究所,2006年至今就职于北京大学。 IEEE Fellow、ACM Fellow、中国工程院院士。
     他的研究领域为多媒体计算和计算机视觉,包括视频编码、视频分析、多媒体检索、人脸识别、多模态接口和虚拟现实。他最常被引用的工作是基于模型的视频编码与基于特征的对象表达。他先后出版著作七本,合作发表300余篇期刊论文、700余篇国际会议论文。先后多次获得国家科技进步奖、国家技术发明奖、国家自然科学奖等学术奖励。


Title: 待发布

Rodolphe Sepulchre 教授

University of Cambridge, 英国

Abstract

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.

Biography

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.

Richard D. Braatz 教授

Massachusetts Institute of Technology, 美国

Abstract

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.

Biography

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.