### Abstract

Original language | English |
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Title of host publication | 2018 UKACC 12th International Conference on Control (CONTROL) |

Publisher | IEEE |

Pages | 75-80 |

Number of pages | 6 |

ISBN (Print) | 978-1-5386-2864-5 |

DOIs | |

Publication status | Published - 1 Nov 2018 |

Event | 2018 UKACC 12th International Conference on Control (CONTROL): UKACC 2018 - Sheffield, United Kingdom Duration: 5 Sep 2018 → 7 Sep 2018 https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8491726 |

### Conference

Conference | 2018 UKACC 12th International Conference on Control (CONTROL) |
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Country | United Kingdom |

City | Sheffield |

Period | 5/09/18 → 7/09/18 |

Internet address |

### Fingerprint

### Cite this

*2018 UKACC 12th International Conference on Control (CONTROL)*(pp. 75-80). IEEE. https://doi.org/10.1109/CONTROL.2018.8516773

**Box Particle Control for Aerospace Vehicles Guidance Under Failure Probability Constraints.** / Merlinge, Nicolas Jonathan Adrien; Horri, Nadjim; Dahia, Karim; Brusey, James; Piet-Lahanier, Helene.

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

*2018 UKACC 12th International Conference on Control (CONTROL) .*IEEE, pp. 75-80, 2018 UKACC 12th International Conference on Control (CONTROL), Sheffield, United Kingdom, 5/09/18. https://doi.org/10.1109/CONTROL.2018.8516773

}

TY - GEN

T1 - Box Particle Control for Aerospace Vehicles Guidance Under Failure Probability Constraints

AU - Merlinge, Nicolas Jonathan Adrien

AU - Horri, Nadjim

AU - Dahia, Karim

AU - Brusey, James

AU - Piet-Lahanier, Helene

PY - 2018/11/1

Y1 - 2018/11/1

N2 - 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.

AB - 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.

UR - https://ieeexplore.ieee.org/document/8516773

U2 - 10.1109/CONTROL.2018.8516773

DO - 10.1109/CONTROL.2018.8516773

M3 - Conference proceeding

SN - 978-1-5386-2864-5

SP - 75

EP - 80

BT - 2018 UKACC 12th International Conference on Control (CONTROL)

PB - IEEE

ER -