2020
DOI: 10.1109/access.2020.2988773
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Comparison of Linear and Nonlinear Methods for Distributed Control of a Hierarchical Formation of UAVs

Abstract: 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 … Show more

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Cited by 22 publications
(17 citation statements)
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“…Nonlinear control methods can substantially expand the domain of controllable flights compared to linear methods. A comparison has been made between linear and non-linear methods to control the UAVs in [7]. In the following, a survey of quadcopter common control algorithms is summarized.…”
Section: Related Work Systems and Algorithmsmentioning
confidence: 99%
“…Nonlinear control methods can substantially expand the domain of controllable flights compared to linear methods. A comparison has been made between linear and non-linear methods to control the UAVs in [7]. In the following, a survey of quadcopter common control algorithms is summarized.…”
Section: Related Work Systems and Algorithmsmentioning
confidence: 99%
“…Environmental disturbances exist in all industrial systems and have a huge impact over especially UAVs and therefore are one of the key factors in the design of stability controllers of such systems. This environmental disturbance, such as safe and controlled landing of the UAVs under dynamic conditions such as oscillatory or moving platforms [131] or maintaining the geometric configuration of multiple or swarm of UAVs [132] or wind effect [133], is estimated by the designed controller and then a feedback control action is taken based on that. Different methodologies or algorithms designed to deal with such uncertainties have the common goal of estimation of uncertainties or disturbances to design a compensation controller that minimises their effect on the system.…”
Section: E Environmental Effectsmentioning
confidence: 99%
“…These agents are deployed at the distributing substations and manage the number of microgrids, and vehicle clusters attached to it. • Microgrid Aggregation Unit agent and the Clusters of Vehicles Controller agent: These are virtual agents located at secondary substations (MV/LV) providing mainly ancillary communication services [37]. • Vehicle Controller agent: This agent acts and takes decisions autonomously to fulfil the requirements of the EV charging.…”
Section: Agents In the Mas Frameworkmentioning
confidence: 99%
“…It represents a service providing company (as a retailer), and its responsible is to buy the energy or electricity from the wholesale market. Electric Public Transport Agent: Used to predict and simulate the behaviour of the electric public transports (like taxi and bus) and to maximise the profit. Charging Station Agent: The main task of this agent is to provide all the charging‐related services to the vehicles and report the changing demand to the power grid agent to maximise the profit percent. Power Grid Agent: Receives power load and demand responses and adjusts the proper distribution and prices correspondingly. Monitor Agent: Displays and monitors the status of all the stations and EVs via GUI. Time Agent: Allow user to start and stop simulation and predicts the time of the simulation case. Map Agent: Provides map service and helps to find the shortest path between two different places in its GUI panel. Regional Aggregation Unit (RAU) agent: This agent sets the pricing policies to meet the optimum EV charging requirements in the grid network [36]. These agents are deployed at the distributing substations and manage the number of microgrids, and vehicle clusters attached to it. Microgrid Aggregation Unit agent and the Clusters of Vehicles Controller agent: These are virtual agents located at secondary substations (MV/LV) providing mainly ancillary communication services [37]. Vehicle Controller agent: This agent acts and takes decisions autonomously to fulfil the requirements of the EV charging.…”
Section: Communication Network Architecture For Sgmentioning
confidence: 99%