MMAE Seminar - Dr. Baisravan HomChaudhuri - Fuel Economic and Safe Autonomous and Semi-autonomous Control of Vehicular Systems

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John T. Rettaliata Engineering Center, Room 104, 10 West 32nd Street, Chicago, IL 60616
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Armour College of Engineering's Mechanical, Materials & Aerospace Engineering Department will welcome Dr. Baisravan HomChaudhuri, a Postdoctoral Fellow in the Electrical and Computer Engineering Department of University of New Mexico, on Wednesday, March 8th, to present his lecture, Fuel Economic and Safe Autonomous and Semi-autonomous Control of Vehicular Systems.

Abstract

Safety, fuel economy, traffic mobility, and vehicle emissions are some of the major concerns of the current transportation system. To address these issues, there is a need for advanced control theoretic tools for safe, efficient, and reliable operation of vehicular systems, especially in presence of human drivers. In this context, two problems are addressed in this research: (i) fuel economic semi-autonomous control of the connected vehicles, and (ii) safe vehicular navigation in presence of dynamic stochastically moving obstacles (vehicles). The former topic deals with fuel economic controller synthesis for a personalized driver assistance system where the controller gives command to a human driver to follow considering the human error that can be injected. In the second topic, safe and reliable controller is designed by exploiting forward stochastic reachability analysis for obstacle prediction so that system safety can be guaranteed in a probabilistic sense.

Biography

Baisravan HomChaudhuri is currently a Postdoctoral Fellow in the Electrical and Computer Engineering Department of University of New Mexico. He received his PhD (2013) and MS (2010) in Mechanical Engineering from University of Cincinnati, and BE (2007) in Electrical Engineering from Jadavpur University (India). He previously held a Postdoctoral position at Clemson University International Center for Automotive Research. His research interest includes distributed optimization and control, optimal control, model predictive control, estimation methods, motion planning, reachability analysis, robotics, and connected vehicle systems.


Earn Engineering Themes credit in Energy for attending.