Box Particle Control for Aerospace Vehicles Guidance Under Failure Probability Constraints

Nicolas Jonathan Adrien Merlinge, Nadjim Horri, Karim Dahia, James Brusey, Helene Piet-Lahanier

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

Abstract

Determining a constrained optimal trajectory remains tricky when the state suffers non-analytical uncertainty and when the feasible set is non-convex. This paper presents a chance constrained trajectory planning approach, called Box Particle Control (BPC), which guarantees an a priori specified maximum probability of constraints violation along a predicted trajectory. This failure probability is estimated by approximating the state density with a mixture of bounded kernels, defined by weighted box particles, and is used as a constraint in an optimization scheme. Numerical simulations illustrate the performance of BPC, which ensures the constraints satisfaction even for low numbers of box particles. The BPC makes it possible to tackle non-analytic state densities (e.g., multimodalities) and non-convex feasible sets with a higher robustness and a 60% lower computational load than previous approaches in terms of number of elementary operations.
Original languageEnglish
Title of host publication2018 UKACC 12th International Conference on Control (CONTROL)
PublisherIEEE
Pages75-80
Number of pages6
ISBN (Print)978-1-5386-2864-5
DOIs
Publication statusPublished - 1 Nov 2018
Event2018 UKACC 12th International Conference on Control (CONTROL): UKACC 2018 - Sheffield, United Kingdom
Duration: 5 Sep 20187 Sep 2018
https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8491726

Conference

Conference2018 UKACC 12th International Conference on Control (CONTROL)
CountryUnited Kingdom
CitySheffield
Period5/09/187/09/18
Internet address

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  • Cite this

    Merlinge, N. J. A., Horri, N., Dahia, K., Brusey, J., & Piet-Lahanier, H. (2018). Box Particle Control for Aerospace Vehicles Guidance Under Failure Probability Constraints. In 2018 UKACC 12th International Conference on Control (CONTROL) (pp. 75-80). IEEE. https://doi.org/10.1109/CONTROL.2018.8516773