Overview
Leila Bridgeman joined Duke as an assistant professor of mechanical engineering and materials science on January 1, 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 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.
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.
She is also interested in model predictive control (MPC) especially when applied to switched systems. Bridgeman continues to collaborate with colleagues at MERL, enabling the use of MPC in novel applications including networked systems, vehicle control, heating, and ventilation.
Current Appointments & Affiliations
Recent Publications
Iterative, local, small-signal L2 stability analysis of nonlinear constrained systems
Journal Article
Systems and Control Letters
·
May 1, 2025
This paper provides a method to analyze the local, small-signal L2-gain of control-affine nonlinear systems on compact sets via iterative semi-definite programs. First, a continuous piecewise affine storage function and the corresponding upper bound on the ...
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Dissipative imitation learning for discrete dynamic output feedback control with sparse data sets
Journal Article International Journal of Robust and Nonlinear Control · September 10, 2024 Imitation learning enables synthesis of controllers for systems with complex objectives and uncertain plant models. However, ensuring an imitation learned controller is stable requires copious amounts of data and/or a known plant model. In this paper, we e ... Full text CiteSystematic, Lyapunov-Based, Safe and Stabilizing Controller Synthesis for Constrained Nonlinear Systems
Journal Article IEEE Transactions on Automatic Control · May 1, 2024 A controller synthesis method for state- and input-constrained nonlinear systems is presented that seeks continuous piecewise affine (CPA) Lyapunov-like functions and controllers simultaneously. Nonconvex optimization problems are formulated on triangulate ... Full text CiteRecent Grants
Collaborative Research: Direct Data-Driven Positive, Control, and Contractive Invariant Sets
ResearchPrincipal Investigator · Awarded by National Science Foundation · 2023 - 2026Title: NRT-FW-HTF: NSF Traineeship in the Advancement of Surgical Technologies
Inst. Training Prgm or CMECo-Principal Investigator · Awarded by National Science Foundation · 2021 - 2026Robust, Distributed Control for Coordinating Aerial Vehicles
ResearchPrincipal Investigator · Awarded by Office of Naval Research · 2022 - 2025View All Grants