Gaussian Mixture Penalty for Trajectory Optimization Problems - Archive ouverte HAL Access content directly
Journal Articles Journal of Guidance, Control, and Dynamics Year : 2019

Gaussian Mixture Penalty for Trajectory Optimization Problems

(1, 2, 3) , (1, 3) , (4, 5) , (2)
1
2
3
4
5

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.
Fichier principal
Vignette du fichier
JGCD.pdf (548.05 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01819749 , version 1 (20-06-2018)

Identifiers

Cite

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

Altmetric

Share

Gmail Facebook Twitter LinkedIn More