Dr. Bridgeman, Leila
Seminar Information
The desire to optimize performance is pervasive across engineering disciplines - and society for that matter – and the advent of ‘big data’ and learning-based methods have brought leaps in performance in many areas. However, the sole pursuit of performance is not an option in control systems, where highly aggressive, ``optimal’’ controllers can overreact to the external disturbances and disruptions we encounter in the real world. The alternative to this is robust control, but it is notoriously conservative and typically requires careful, model-based analysis that is challenging for nonlinear and networked systems. This talk will discuss how a distinctive perspective on the foundational results of input-output stability theory can help resolve these issues, assuring stability and even improving performance in the learning context.
Leila Bridgeman earned B.Sc. and M.Sc. degrees in Applied Mathematics in 2008 and 2010 from McGill University, Montreal, QC, Canada, where she completed her Ph.D. in Mechanical Engineering, earning McGill’s 2016 D.W. Ambridge Prize for outstanding dissertation in the physical sciences and engineering. 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, MA. She is now an assistant professor of Mechanical Engineering and Materials Science.
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, input-output stability theory, and set invariance can be applied through practical, computationally-tractable algorithms. Resulting publications have considered applications of this work to robotic, process control, and time-delay systems and the development of autonomous ultrasound robotics.