Prediction of Moving Targets and Adaptive Avoidance in Hybrid PSO-MPC for a Swarm of UAV’s
DOI:
https://doi.org/10.18372/1990-5548.84.20197Keywords:
UAV swarms, moving obstacle avoidance, particle swarm algorithm, model predictive control, trajectory prediction, formation formation, repulsive forces, adaptive control, cohesive flight, formation and avoidance, flight safety, swarming algorithms, intelligent systems, remote forecasting, cooperative controlAbstract
The paper proposes a hybrid approach to the safe control of a multicopter swarm in the presence of two moving obstacles based on a combination of the particle swarm algorithm and model predictive control. The first stage of the algorithm is a global search for new target positions of subgroup centers using particle swarm algorithm based on predicted data, which allows the front subgroup to smoothly climb and avoid the danger zone. The second stage is the local adjustment of the movement of each vehicle within the model predictive control, taking into account dynamic constraints, which ensures accurate adherence to the calculated targets and prevents formation disruption. Simulation experiments demonstrate that the developed algorithm ensures coordinated maneuvers of all subgroups, timely avoidance of both moving threats, and return to the original formation without sudden jumps in altitude or chaotic behavior.
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