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Nonlinear distributed model predictive flocking with obstacle avoidance
Citation Link: https://doi.org/10.15480/882.9032
Publikationstyp
Conference Paper
Publikationsdatum
2023
Sprache
English
Enthalten in
Volume
56
Issue
2
Start Page
3794
End Page
3799
Citation
IFAC-PapersOnLine 56 (2): 3794-3799 (2023)
Contribution to Conference
Publisher DOI
Publisher
Elsevier BV
Peer Reviewed
true
In this paper, we present a framework for nonlinear distributed model predictive flocking with obstacle avoidance, the pursuit of group objectives, and input constraints. While most existing predictive flocking frameworks are only applicable to agents with double-integrator dynamics, we propose a general framework for nonlinear agents that furthermore allows for the independent tuning of cohesive and repulsive inter-agent forces. To reduce the computational complexity, the resulting nonlinear program is solved as a sequential quadratic program with a limited number of iterations. The performance of the proposed algorithms is demonstrated in simulation and compared to a non-predictive flocking algorithm.
Schlagworte
Consensus
Control over networks
Graph-based methods for networked systems
Multi-agent systems |Time-varying systems
Networked systems
DDC Class
620: Engineering
Publication version
publishedVersion
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