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 language | English |
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Title of host publication | Proceedings of the 12th International Micro Air Vehicle Conference |
Editors | Jose Martinez-Carranza |
Pages | 28-34 |
Number of pages | 7 |
Publication status | Published - 2021 |
Event | 12th International Micro Air Vehicle Conference - Puebla, Mexico Duration: 17 Nov 2021 → 19 Nov 2021 Conference number: 12 |
Conference
Conference | 12th International Micro Air Vehicle Conference |
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Abbreviated title | IMAV 2021 |
Country/Territory | Mexico |
City | Puebla |
Period | 17/11/21 → 19/11/21 |
Fingerprint
Dive into the research topics of 'Nonlinear model predictive control for improving range-based relative localization by maximizing observability'. Together they form a unique fingerprint.Prizes
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Best Conference Paper Award of the 12th International Micro Air Vehicle Conference 2021
Li, S. (Recipient), de Wagter, C. (Recipient) & de Croon, G.C.H.E. (Recipient), 19 Nov 2021
Prize: Prize (including medals and awards)
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