Dakota and Pyomo for Closed and Open Box Controller Gain Tuning.

Williams, Kyle; Wilbanks, James; Schlossman, Rachel; Kozlowski, David; Parish, Julie

doi:10.1109/CDC45484.2021.9683279

Dakota and Pyomo for Closed and Open Box Controller Gain Tuning.

Conference · Wed Sep 01 00:00:00 EDT 2021

DOI:https://doi.org/10.1109/CDC45484.2021.9683279· OSTI ID:1890623

Williams, Kyle; Wilbanks, James; Schlossman, Rachel; Kozlowski, David; Parish, Julie

Abstract not provided.

Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

DOE Contract Number:: NA0003525

OSTI ID:: 1890623

Report Number(s):: SAND2021-12149C; 700479

Country of Publication:: United States

Language:: English

References (8)

pyomo.dae: a modeling and automatic discretization framework for optimization with differential and algebraic equations Nicholson, Bethany; Siirola, John D.; Watson, Jean-Paul Mathematical Programming Computation, Vol. 10, Issue 2 https://doi.org/10.1007/s12532-017-0127-0	journal	December 2017
The MATLAB ODE Suite Shampine, Lawrence F.; Reichelt, Mark W. SIAM Journal on Scientific Computing, Vol. 18, Issue 1 https://doi.org/10.1137/S1064827594276424	journal	January 1997
Open-Source, Object-Oriented, Multi-Phase Pseudospectral Optimization Using Pyomo Schlossman, Rachel; Williams, Kyle; Kozlowski, David AIAA Scitech 2021 Forum https://doi.org/10.2514/6.2021-1951	conference	January 2021
Design of robust dynamic inversion control laws using multi-objective optimization Looye, Gertjan; Joos, Hans-Dieter AIAA Guidance, Navigation, and Control Conference and Exhibit https://doi.org/10.2514/6.2001-4285	conference	August 2001
An Introduction to Trajectory Optimization: How to Do Your Own Direct Collocation Kelly, Matthew SIAM Review, Vol. 59, Issue 4 https://doi.org/10.1137/16M1062569	journal	January 2017
Evolutionary algorithms in control systems engineering: a survey Fleming, P. J.; Purshouse, R. C. Control Engineering Practice, Vol. 10, Issue 11 https://doi.org/10.1016/S0967-0661(02)00081-3	journal	November 2002
Large-scale nonlinear programming using IPOPT: An integrating framework for enterprise-wide dynamic optimization Biegler, L. T.; Zavala, V. M. Computers & Chemical Engineering, Vol. 33, Issue 3 https://doi.org/10.1016/j.compchemeng.2008.08.006	journal	March 2009
Pyomo — Optimization Modeling in Python Hart, William E.; Laird, Carl D.; Watson, Jean-Paul Springer Optimization and Its Applications https://doi.org/10.1007/978-3-319-58821-6	book	January 2017

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