Formation control of robotized aerial vehicles based on consensus-based algorithms

Stojković, Ivan; Katić, Duško

doi:10.5937/fmet1704559s

Cited by 7 publications

(3 citation statements)

References 10 publications

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“…Ref. [ 25 ] suggests a second-order consensus algorithm to follow a specified external reference in their work, whereas [ 26 ] frames the formation control problem as a position control problem to be solved. In contrast, spacecraft formation vehicles use a mechanism for robust attitude control, as described in [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Path Planning and Formation Control for UAV-Enabled Mobile Edge Computing Network

Choutri

Lagha

Meshoul

et al. 2022

Sensors

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Recent developments in unmanned aerial vehicles (UAVs) have led to the introduction of a wide variety of innovative applications, especially in the Mobile Edge Computing (MEC) field. UAV swarms are suggested as a promising solution to cope with the issues that may arise when connecting Internet of Things (IoT) applications to a fog platform. We are interested in a crucial aspect of designing a swarm of UAVs in this work, which is the coordination of swarm agents in complicated and unknown environments. Centralized leader–follower formations are one of the most prevalent architectural designs in the literature. In the event of a failed leader, however, the entire mission is canceled. This paper proposes a framework to enable the use of UAVs under different MEC architectures, overcomes the drawbacks of centralized architectures, and improves their overall performance. The most significant contribution of this research is the combination of distributed formation control, online leader election, and collaborative obstacle avoidance. For the initial phase, the optimal path between departure and arrival points is generated, avoiding obstacles and agent collisions. Next, a quaternion-based sliding mode controller is designed for formation control and trajectory tracking. Moreover, in the event of a failed leader, the leader election phase allows agents to select the most qualified leader for the formation. Multiple possible scenarios simulating real-time applications are used to evaluate the framework. The obtained results demonstrate the capability of UAVs to adapt to different MEC architectures under different constraints. Lastly, a comparison is made with existing structures to demonstrate the effectiveness, safety, and durability of the designed framework.

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Section: Introductionmentioning

confidence: 99%

Path Planning and Formation Control for UAV-Enabled Mobile Edge Computing Network

Choutri

Lagha

Meshoul

et al. 2022

Sensors

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“…Recently, the formation consistency of the vehicle platoon system has been widely considered. It has been applied to deal with consistency of formation control problems (Ren, 2007 ; Stojković and Katić, 2017 ; Wang et al, 2017 ; Li et al, 2018 ). Bela Lantos and Gyorgy Max achieved the formation consistency of unmanned ground vehicles by using a two-trajectory non-linear dynamic model (Lantos and Max, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Self-Triggered Consensus of Vehicle Platoon System With Time-Varying Topology

Wang

Guo

et al. 2020

Front. Neurorobot.

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This paper focuses on the consensus problem of a vehicle platoon system with time-varying topology via self-triggered control. Unlike traditional control methods, a more secure event-triggered controller considering the safe distance was designed for the vehicle platoon system. Then, a Lyapunov function was designed to prove the stability of the platoon system. Furthermore, based on the new event-triggered function, a more energy efficient self-triggered control strategy was designed by using the Taylor formula. The new self-triggered control strategy can directly calculate the next trigger according to the state information of the last trigger. It avoids continuous calculation and measurement of vehicles. Finally, the effectiveness of the proposed two self-triggered control strategies were verified by numerical simulation experiments.

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“…A basic autopilot system includes trim sensors and an onboard processor. Because of the high non-linearity of the plane's dynamics, they are necessarymany advanced techniques, including PID control, optimal control methods [1][2], neural networks [3], fuzzy logic [4], and other in order to ensure the desired trajectory [5]. Lowlevel loops (which control flight) can occur 100 times per second and higher-level loops can be requested with lengthsaround a second.…”

Section: Introductionmentioning

confidence: 99%

Control design for an under-actuated UAV model

De¹,

Guida²

2018

FME Transactions

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Aerial Vehicles (UAV), are increasingly being adapted and used in civil contexts, with particular specialization in the monitoring of the territory for environmental purposes, civil protection or possibly control and inspection of inaccessible areas or dangerous sites. This concerns, in particular, the adaptation of mini-UAVs, small devices that, due to their manageability and costs, can be allocated for public services.For this reason, it was decided to develop a multibody model of a quadrotor copter in the presence of wind fields by using SimScape Multibody Environment. The quadrotor is modelled as a non-linear underactuated and strongly coupled system. For the dynamic analysis, aerodynamic effect of drag, the thrust due to the propellers and external wind fields are considered. The evaluation of the needed thrust for prescribed trajectories gives indications about the type of propellers and actuator characteristics to be installed on the UAV.

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Formation control of robotized aerial vehicles based on consensus-based algorithms

Abstract: In this paper, consensus based formation control of multi

Cited by 7 publications

References 10 publications

Path Planning and Formation Control for UAV-Enabled Mobile Edge Computing Network

Path Planning and Formation Control for UAV-Enabled Mobile Edge Computing Network

Self-Triggered Consensus of Vehicle Platoon System With Time-Varying Topology

Control design for an under-actuated UAV model

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