A Distributed Control of Movements and Fuzzy Logic-Based Task Allocation for a Swarm of Autonomous Agents

Lucas, Pedro; Loayza, Kleber; Peláez, Enrique

doi:10.1109/fuzz-ieee.2018.8491460

Cited by 2 publications

(1 citation statement)

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“…In [15], an event-triggered distributed Model Predictive Control (MPC) technique for formation control of three drones is studied, where each drone shares information with its neighbours. In [16], a fuzzy logic approach for task assignment and controlling a swarm of agents, representing UAVs, in a decentralized environment is examined, and the results of collision attempts and completion times between the distributed and centralized strategies through simulations are compared. The central control design is based on finite state machines, and the proposed solution is simulated using the Unity3D engine.…”

Section: Related Workmentioning

confidence: 99%

Comparison of Linear and Nonlinear Methods for Distributed Control of a Hierarchical Formation of UAVs

et al. 2020

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A key problem in cooperative robotics is the maintenance of a geometric configuration during movement. As a solution for this, a multi-layered and distributed control system is proposed for the swarm of drones in the formation of hierarchical levels based on the leader-follower approach. The complexity of developing a large system can be reduced in this way. To ensure the tracking performance and response time of the ensemble system, nonlinear and linear control designs are presented; (a) Sliding Mode Control connected with Proportional-Derivative controller and (b) Linear Quadratic Regular with integral action respectively. The safe travel distance strategy for collision avoidance is introduced and integrated into the control designs for maintaining the hierarchical states in the formation. Both designs provide a rapid adoption with respect to their settling time without introducing oscillations for the dynamic flight movement of vehicles in the cases of (a) nominal, (b) plant-model mismatch, and (c) external disturbance inputs. Also, the nominal settling time of the swarm is improved by 44% on average when using the nonlinear method as compared to the linear method. Furthermore, the proposed methods are fully distributed so that each UAV autonomously performs the feedback laws in order to achieve better modularity and scalability. INDEX TERMS Unmanned aerial vehicles (UAVs), distributed control, hierarchical systems.

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Section: Related Workmentioning

confidence: 99%