Sensitivity-Driven Experimental Design to Facilitate Control of Dynamical Systems

Joseph Hart (), Bart Bloemen Waanders (), Lisa Hood () and Julie Parish ()
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Joseph Hart: Scientific Machine Learning
Bart Bloemen Waanders: Scientific Machine Learning
Lisa Hood: Navigation, Guidance & Ctrl II
Julie Parish: Autonomy for Hypersonics

Journal of Optimization Theory and Applications, 2023, vol. 196, issue 3, No 4, 855-881

Abstract: Abstract Control of nonlinear dynamical systems is a complex and multifaceted process. Essential elements of many engineering systems include high-fidelity physics-based modeling, offline trajectory planning, feedback control design, and data acquisition strategies to reduce uncertainties. This article proposes an optimization-centric perspective which couples these elements in a cohesive framework. We introduce a novel use of hyper-differential sensitivity analysis to understand the sensitivity of feedback controllers to parametric uncertainty in physics-based models used for trajectory planning. These sensitivities provide a foundation to define an optimal experimental design which seeks to acquire data most relevant in reducing demand on the feedback controller. Our proposed framework is illustrated on the Zermelo navigation problem and a hypersonic trajectory control problem using data from NASA’s X-43 hypersonic flight tests.

Keywords: Hyper-differential sensitivity analysis; Optimal experimental design; Optimal control; Trajectory planning (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10957-023-02172-w

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