Nonlinear model predictive control for improving range-based relative localization by maximizing observability

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Abstract

Wireless ranging measurements have been proposed for enabling multiple Micro Air Vehicles (MAVs) to localize with respect to each other. However, the high-dimensional relative states are weakly observable due to the scalar distance measurement. Hence, the MAVs have degraded relative localization and control performance under unobservable conditions as can be deduced by the Lie derivatives. This paper presents a nonlinear model predictive control (NMPC) by maximizing the determinant of the observability matrix in order to generate optimal control inputs, which also satisfy constraints including multirobot tasks, input limitation, and state bounds. Simulation results validate the localization and control efficacy of the proposed MPC method for range-based multi-MAV systems with weak observability, which has faster convergence time and more accurate localization compared to previously proposed random motions.
Original languageEnglish
Title of host publicationProceedings of the 12th International Micro Air Vehicle Conference
EditorsJose Martinez-Carranza
Pages28-34
Number of pages7
Publication statusPublished - 2021
Event12th International Micro Air Vehicle Conference - Puebla, Mexico
Duration: 17 Nov 202119 Nov 2021
Conference number: 12

Conference

Conference12th International Micro Air Vehicle Conference
Abbreviated titleIMAV 2021
Country/TerritoryMexico
CityPuebla
Period17/11/2119/11/21

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