University of California, Berkeley
Lino Guzzella has been a full professor at ETH Zurich, Switzerland since 1999. After receiving his mechanical engineering diploma in 1981 and his doctoral degree in 1986, both from ETH, he has held several positions in industry and academia.
With his research group he focuses on novel approaches in system dynamics and in the control of energy conversion systems. Control-oriented systems modeling, dynamic optimization, and feedback control design methods are his main areas of research. He places a particular emphasis on the minimization of fuel consumption and pollutant emission of automotive propulsion systems. In teaching, he has been successfully promoting project- and team-based learning approaches. Among the awards he received are the IEEE Control Systems Magazine Outstanding Paper Award, the SAE Arch T. Colwell Merit Award and the Ralph R. Teetor Educational Award, the IMechE Thomas Hawksley Medal and Crompton Lancaster Medal, and the Energy Globe Award. Lino Guzzella has published more than 100 research articles as well as two research textbooks (Modeling and Control of IC Engine Systems, Springer Verlag, 2004, and Vehicle Propulsion Systems, 2nd Ed., Springer Verlag, 2007). He is a consultant to several tier-one automotive companies and holds several patents on automotive control systems.
Individual mobility is closely linked to the welfare of any society. Not surprisingly, the number of automobiles has been inexorably increasing and is likely to double in the next twenty years. Clearly, this development creates many benefits and economic opportunities, but also many problems, such as air pollution, traffic fatalities, increased energy consumption and carbon dioxide emission.
In this talk the relevance of these problems will be prioritized and some of the most likely technological solutions will be presented. One key point is that in most – if not all – of these approaches automatic control systems will be an enabling factor, without which no true breakthroughs are possible. After these rather general remarks, the following some examples will be presented to show what typical problem setups are to be faced and what methods are to be used to tackle these problems.
Roberto Horowitz received a B.S. degree with highest honors in 1978 and a Ph.D. degree in 1983 in mechanical engineering from the University of California at Berkeley. In 1982 he joined the Department of Mechanical Engineering at the University of California at Berkeley, where he is currently a Professor. Dr. Horowitz teaches and conducts research in the areas of adaptive, learning, nonlinear and optimal control. His current research interests include: Micro-mechatronics, control of computer disk file systems, robotics, mechatronics of smart exercise machines and paper handling devices, and Intelligent Vehicle and Highway Systems (IVHS).
Vehicular traffic congestion remains one of the major world-wide sources of productivity and efficiency loss, wasteful energy consumption, and avoidable air pollution. For example it is estimated that in 2007, congestion caused urban Americans to travel an additional 4.2 billion hours and to purchase an extra 2.9 billion gallons of fuel. In this talk I will describe a set of modeling and simulation Tools for Operational Planning (TOPL) developed to provide quick and quantitative assessments of the benefits that Transportation Management Center (TMC) control policies can provide on freeway corridors, in order to decrease congestion. A freeway corridor typically comprises a 40 kilometer freeway segment on a highly populated urban area, together with its adjoining major urban streets or arterials. The movement of vehicles in a corridor is regulated by programmable field control elements including arterial intersection signals, ramp-metering signals, and message signs that announce emergency conditions, set speed limits and tolls, and provide driver information. Traffic data is primarily collected through inductive loop detectors buried roughly every kilometer along the freeways' payment, as well as detectors located in some of the major corridor arterials. TOPL contains a self-calibrated Cell Transmission Model (CTM) traffic macroscopic simulator. This simulator relies on a well-accepted theoretical model of traffic flow; it is parsimonious and does not require parameters that cannot be estimated from traffic data; and has been tested for reliability on several freeways. Moreover, it is fast, running several hundred times faster than real time, which can be used with real-time measurements and statistically predicted short term future traffic demands to keep track of the current freeway traffic state, as well as make short-term predictions. I will also discuss the qualitative behavior of a single freeway based on the CTM, and will focus on several results regarding the structure and stability of the set of equilibrium states in single freeway, including the fact that the freeway decomposes into disjoint contiguous segments demarcated by bottleneck links, with each segment having qualitatively the same behavior. These properties will be further explored in the formulation of traffic responsive and coordinated ramp-metering policies, including a coordinated policy that minimizes travel time, model calibration and missing on-ramp imputation techniques, and congestion and state estimation techniques.
Francesco Bullo received the Laurea degree in Electrical Engineering from the University of Padova in 1994, and the Ph.D. degree in Control and Dynamical Systems from the California Institute of Technology in 1999. From 1998 to 2004, he was affiliated with the University of Illinois at Urbana-Champaign. He is currently a Professor with the Mechanical Engineering Department at the University of California, Santa Barbara. He is the coauthor of the book "Geometric Control of Mechanical Systems" (Springer, 2004) and of the book "Distributed Control of Robotic Networks" (Princeton, 2009). His research interests include cooperative control, vehicle routing, and motion planning for autonomous robots, as well as geometric control of mechanical systems.
Motion coordination is an extraordinary phenomenon in biological systems and a powerful tool in man-made systems; although individual agents have no global system knowledge, complex behaviors emerge from local interactions. This talk focuses on robotic networks, that is, group of robots that communicate and coordinate their motions to perform useful tasks. Example tasks are how to respond to service requests in an environment, how to deploy sensor nodes in locations of interest, and how to partition an environment among cooperating agents. For these tasks, we propose a comprehensive collection of adaptive and distributed algorithms, including a novel a novel deployment and partitioning algorithm with minimal communication requirements. Our approach integrates concepts from queuing and stochastic analysis, geometric optimization, and nonlinear stability theory.
