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 proceedingpeer-review


    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)
    Number of pages6
    ISBN (Print)978-1-5386-2864-5
    Publication statusPublished - 1 Nov 2018
    EventUKACC 12th International Conference on Control 2018 - Sheffield, United Kingdom
    Duration: 5 Sept 20187 Sept 2018
    Conference number: 12


    ConferenceUKACC 12th International Conference on Control 2018
    Abbreviated titleCONTROL 2018
    Country/TerritoryUnited Kingdom
    Internet address


    Dive into the research topics of 'Box Particle Control for Aerospace Vehicles Guidance Under Failure Probability Constraints'. Together they form a unique fingerprint.

    Cite this