Skip to Main content Skip to Navigation
Journal articles

Gaussian Mixture Penalty for Trajectory Optimization Problems

Abstract : We consider the task of solving an aircraft trajectory optimization problem where the system dynamics have been estimated from recorded data. Additionally, we want to avoid optimized trajectories that go too far away from the domain occupied by the data, since the model validity is not guaranteed outside this region. This motivates the need for a proximity indicator between a given trajectory and a set of reference trajectories. In this presentation, we propose such an indicator based on a parametric estimator of the training set density. We then introduce it as a penalty term in the optimal control problem. Our approach is illustrated with an aircraft minimal consumption problem and recorded data from real flights. We observe in our numerical results the expected trade-off between the consumption and the penalty term.
Complete list of metadatas

Cited literature [16 references]  Display  Hide  Download

https://hal.inria.fr/hal-01819749
Contributor : Cédric Rommel <>
Submitted on : Wednesday, June 20, 2018 - 8:41:35 PM
Last modification on : Saturday, April 11, 2020 - 2:07:03 AM
Document(s) archivé(s) le : Tuesday, September 25, 2018 - 2:46:26 PM

File

JGCD.pdf
Files produced by the author(s)

Identifiers

Citation

Cédric Rommel, Frédéric Bonnans, Pierre Martinon, Baptiste Gregorutti. Gaussian Mixture Penalty for Trajectory Optimization Problems. Journal of Guidance, Control, and Dynamics, American Institute of Aeronautics and Astronautics, 2019, 42 (8), pp.1857--1862. ⟨10.2514/1.G003996⟩. ⟨hal-01819749⟩

Share

Metrics

Record views

470

Files downloads

889