Announcements

Colloquium on Digital Transformation Science

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  October 7, 3 pm CT Hierarchical Control for Cyber-Physical Systems and Applications to Traffic Management Universal Laws and Architectures and Their Fragilities John Doyle, Jean-Lou Chameau Professor of Control and Dynamical Systems, Electrical Engineering, and BioEngineering, California Institute of TechnologyMurat Arcak, Professor of Electrical Engineering and Computer Sciences,  University of California, Berkeley REGISTER FOR ZOOM WEBINAR Control of vehicle traffic management cyber-physical systems is invariably organized in a hierarchical structure that consists of multiple layers of feedback such as network, road link, and vehicle control. Using traffic management as a running example, this talk will present an integrated approach to designing these layers, thereby enabling a rigorous framework to provide system-level guarantees for the whole control stack. An example of this approach is symbolic control, which generates supervisory control actions to fulfill complex requirements expressed in temporal logic. In traffic management, symbolic control enables us to depart from steady state signal timing plans, and to develop reactive signaling schemes by first expressing finite horizon goals, such as dissipating queues and avoiding saturation, in temporal logic. Moving up to the network layer, we will next present a game theoretic analysis for routing, which takes into account the nonequilibrium dynamics resulting from the drivers’ continual revisions of their routes. We will introduce tools to deal with such dynamics and illustrate them on mixed-autonomy traffic. The past year unfortunately highlighted intrinsic and systemic unsustainability and fragilities in our society and technologies. While detailed mechanisms underlying “systemic fragilities” in immune, medical, computing, social, legal, energy, and transportation systems are incredibly diverse, all are enabled by shared universal features of their architectures, which are largely ad hoc historical artifacts. AI has many well-known fragilities, but outside social media has not so far contributed substantially to the catastrophes unfolding in these systems. This is poised to change dramatically. We need to more systematically design architectures that produce more robust and sustainable systems, including allowing higher layer learning and lower layer efficiencies to contribute. I’ll sketch the basic concepts of laws, layers, levels, and speed-efficiency-accuracy-flexibility trade-offs  (SEAFTs), diversity-enabled sweet spots (DeSS), how crucial hardware layer constraints on sparsity, locality, and delay limit system layer functionality, and how proper layering can mitigate this via DeSS. Examples include all our tech nets, layered brains (e.g., throwing and hitting 100 mph fastballs), layered immunity augmented by medicine and policy (and insights into the current pandemic), systemic legal fragilities and the 14th amendment, cascading failures in energy, climate change, language and its hijacking in social media, encouraging animal models for social architectures, and wildfire ecosystems. John Doyle is the Jean-Lou Chameau Professor at the California Institute of Technology. He earned his BS and MS degrees in electrical engineering from MIT. He received his doctorate in mathematics from the University of California, Berkeley. His research interests are in integrated theory foundations and architectures for complex networks that enable efficiency and robustness, with applications to tech, bio, neuro, and social systems and an emphasis on the impact on control performance due to delays, sparsity, locality, and saturations in sensors, actuators, communications, and computing components, and how these arise in and challenge bio, neuro, tech, and social system design. He has few academic awards but when younger had many regional, national, and world records and championships in various sports, and he is known for fantastic students and colleaguesMurat Arcak, a professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley,  received his PhD degree from the University of California, Santa Barbara in 2000. He received a CAREER Award from the National Science Foundation in 2003, the Donald P. Eckman Award from the American Automatic Control Council in 2006, the Control and Systems Theory Prize from the Society for Industrial and Applied Mathematics (SIAM) in 2007, and the Antonio Ruberti Young Researcher Prize from the IEEE Control Systems Society in 2014. He is a Fellow of IEEE and the International Federation of Automatic Control (IFAC).

  
  

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