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Open-source, object-oriented, multi-phase pseudospectral optimization using pyomo

AIAA Scitech 2021 Forum

Schlossman, Rachel S.; Williams, Kyle R.; Kozlowski, David M.; Parish, Julie M.

Multi-phase, pseudospectral optimization is employed in a variety of applications, but many of the world-class optimization libraries are closed-source. In this paper we formulate an open-source, object-oriented framework for dynamic optimization using the Pyomo modeling language. This strategy supports the reuse of common code for rapid, error-free model development. Flexibility of our framework is demonstrated on a series of dynamic optimization problems, including multi-phase trajectory optimization using highly accurate pseudospectral methods and controller gain optimization in the presence of stability margin constraints. We employ numerical procedures to improve convergence rates and solution accuracy. We validate our framework using GPOPS-II, a commercial, MATLAB-based optimization program, for a vehicle ascent problem. The trajectory results show close alignment with this state-of-the-art optimization suite.

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TYPE Conference Paper YEAR 2021
ScopusOSTIDOI

Dakota and Pyomo for Closed and Open Box Controller Gain Tuning

Proceedings of the IEEE Conference on Decision and Control

Williams, Kyle R.; Wilbanks, James J.; Schlossman, Rachel S.; Kozlowski, David M.; Parish, Julie M.

Pyomo and Dakota are openly available software packages developed by Sandia National Labs. In this tutorial, methods for automating the optimization of controller parameters for a nonlinear cart-pole system are presented. Two approaches are described and demonstrated on the cart-pole example problem for tuning a linear quadratic regulator and also a partial feedback linearization controller. First the problem is formulated as a pseudospectral optimization problem under an open box methodology utilizing Pyomo, where the plant model is fully known to the optimizer. In the next approach, a black-box approach utilizing Dakota in concert with a MATLAB or Simulink plant model is discussed, where the plant model is unknown to the optimizer. A comparison of the two approaches provides the end user the advantages and shortcomings of each method in order to pick the right tool for their problem. We find that complex system models and objectives are easily incorporated in the Dakota-based approach with minimal setup time, while the Pyomo-based approach provides rapid solutions once the system model has been developed.

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TYPE Conference Paper YEAR 2021
ScopusOSTIDOI
11 Results
11 Results