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J. Karl Hedrick

J. Karl Hedrick is the James Marshall Wells Professor of Mechanical Engineering and Director of the Vehicle Dynamics and Control Laboratory at UC Berkeley. He served as the Chair of the Department of Mechanical Engineering at UC Berkeley from 1999-2004 and served as the Director of the UC PATH Research Center, a multi-disciplinary research program located at the Richmond Field Station, from 1997-2003. Before coming to Berkeley, he was a Professor of Mechanical Engineering at the Massachusetts Institute of Technology, from 1974-1988, where he served as Director of the Vehicle Dynamics Laboratory. His research has concentrated on the development of nonlinear control theory and on its application to a broad variety of transportation systems including automated highway systems, powertrain control, embedded software design, formation flight of autonomous vehicles, and active suspension

 
 

Francesco Borrelli (Co-Director)

Francesco Borrelli received the Laurea degree in computer science engineering in 1998 from the University of Naples Federico II, Italy. In 2002 he received the PhD from the Automatic Control Laboratory at ETH-Zurich, Switzerland. He is currently a Professor at the Department of Mechanical Engineering of the University of California at Berkeley, USA. He is the author of more than one hundred publications in the field of predictive control. He is author of the book Constrained Optimal Control of Linear and Hybrid Systems published by Springer Verlag, the winner of the 2009 NSF CAREER Award and the winner of the 2012 IEEE Control System Technology Award. In 2016 he was elected IEEE fellow. Since 2004 he has served as a consultant for major international corporations. He is the founder and CTO of BrightBox Technologies Inc, a company focused on cloud-computing optimization for autonomous systems. He is the co-director of the Hyundai Center of Excellence in Integrated Vehicle Safety Systems and Control at UC Berkeley. His research interests include constrained optimal control, model predictive control and its application to advanced automotive control and energy efficient building operation.