Leila Bridgeman


Assistant Professor
Department of Mechanical Engineering & Materials Science

Leila Bridgeman received B.Sc. and M.Sc. degrees in Applied Mathematics in 2008 and 2010 from McGill University, Montreal, QC, Canada, where she recently completed her Ph.D. in Mechanical engineering. Her doctoral research involved a return to the foundational work of George Zames, exploring how the theory of conic sectors can be used to design controllers that guarantee closed-loop input-output stability when more conventional methods fail to apply. 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.

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 input-output stability theory can be applied to the most challenging of controls problems, including the control of delayed, open-loop unstable, and nonminimum-phase systems. Her focus has been on the development of readily-applicable controller synthesis and stability analysis methods based on the evaluation of linear matrix inequalities (LMIs). Resulting publications have considered applications of this work to robotic, process control, and time-delay systems.

Leila is also interested in model predictive control (MPC) especially when applied to switched systems. She continues to collaborate with colleagues at Mitsubishi Electric Research Labs (MERL), enabling the use of MPC in novel applications including networked systems, vehicle control, heating, and ventilation.

Contact Information:

  • Email Address: leila.bridgeman@duke.edu


PhD Mechanical Engineering, McGill University, 2016

MSc Applied Mathematics, McGill University, 2010

BSc Honours Applied Mathematics, McGill University, 2008

Research Interests:

Robust and optimal control, linear matrix inequalities (LMIs), model predictive control (MPC), delayed systems, input-output stability, passivity