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Advancing Control Theory to Advance Robotics

Leila Bridgeman
Duke University

March 22, 2018 at  2:00 PM
Macdonald Engineering Room 267


The ongoing autonomous systems revolution is bringing control systems to new applications, demanding more versatile, reliable, and responsive controls algorithms. Autonomous driving, spacecraft pointing, indoor temperature and humidity control, and cutting-edge robotics all hinge on the ability of a control system to robustly and reliably regulate system behavior. This seminar will discuss how fundamental theoretical results, both new and old, play a crucial role in addressing these practical challenges. This will include how the revival of existing input-output results can enable improved performance with guaranteed stability, why novel feasibility criteria for switched systems improve cooperative robotics, and the role of novel stability analysis in the development of real-time model predictive control for nonlinear systems.

Biographical sketch:

Leila Bridgeman joined Duke’s Mechanical Engineering and Materials Science department as a postdoctoral researcher in 2017 and became an assistant professor in January 2018. She received B.Sc. and M.Sc. degrees in Applied Mathematics in 2008 and 2010 from McGill University, Montreal. In 2016, she completed a Ph.D. in Mechanical engineering, also at McGill University. Her graduate studies involved research semesters at University of Michigan, University of Bern, and University of Victoria, along with an internship at Mitsubishi Electric Research Laboratories (MERL) in Boston. Leila’s doctoral research extending and applying the foundational work of George Zames was awarded McGill’s 2017 D. W. Ambridge Prize.
Through her research, Leila strives to bridge the gap between theoretical results in robust and optimal control and their use in practice. She explores how the tools of numerical analysis and stability theory can be applied to the most challenging of control problems. Resulting publications have considered applications of this work to robotic, process control, and time-delay systems.