Formation Control Algorithm of Multi-UAV-Based Network Infrastructure

Park, Seong Joon; Kim, Kangho; Kim, Hyun-Soon; Kim, Hwangnam

doi:10.3390/app8101740

Cited by 30 publications

(27 citation statements)

References 37 publications

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“…An example for the non autonomous coupling case which also can be seen as an imposition of the strength of the couplings in a network is found in treatments where deep brain stimulation is performed to treat Alzheimer's, Parkinson's disease, tremor, and so forth, this stimulation is imposed or dictated to the network [18][19][20]. On the other hand, evolving couplings are considered when the nodes define their own coupling strength according to their requirements, this is, the relationships between agents are determined by them, for instance in social networks [21], in problems of formation control of UAV [26] and so forth.…”

Section: Discussionmentioning

confidence: 99%

Synchronization in Time-Varying and Evolving Complex Networks

2020

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In this contribution, we present the synchronization in dynamical complex networks with varying couplings. We identify two kinds of variations—(i) Non autonomous (Time-varying) couplings: where the coupling strength depends exclusively on time, (ii) Autonomous or Varying couplings (evolution) where the coupling strength depends on the behavior of the interconnected systems. The coupling strength in (i) is exogenous whereas in (ii) the coupling strength is endogenous and is defined by the states of the systems in the nodes. The exponential stability of the synchronization is ensured for the non autonomous couplings, due to the imposition of the coupling strength. Whereas, in the case of evolutionary couplings the exponential stability of the synchronization is not guaranteed for all time, due to the couplings are not controlled or imposed. We present an overview of these features in complex networks and illustrated by means of numerical examples.

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Section: Discussionmentioning

confidence: 99%

Synchronization in Time-Varying and Evolving Complex Networks

2020

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“…With this information, the AirBSs estimate the gradient, which points in a direction of increasing network utility J(l) on average. It remains to design suitable functions J m (l) of the form (11). The most direct choice of J m (l) is the rate of the m-th user, which in turn means that J(l) is the expected rate the MUs.…”

Section: B Utility Functions For Non-cooperative Placementmentioning

confidence: 99%

Non-Cooperative Aerial Base Station Placement via Stochastic Optimization

Romero

Leus

2019

2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN)

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Autonomous unmanned aerial vehicles (UAVs) with on-board base station equipment can potentially provide connectivity in areas where the terrestrial infrastructure is overloaded, damaged, or absent. Use cases comprise emergency response, wildfire suppression, surveillance, and cellular communications in crowded events to name a few. A central problem to enable this technology is to place such aerial base stations (AirBSs) in locations that approximately optimize the relevant communication metrics. To alleviate the limitations of existing algorithms, which require intensive and reliable communications among AirBSs or between the AirBSs and a central controller, this paper leverages stochastic optimization and machine learning techniques to put forth an adaptive and decentralized algorithm for AirBS placement without inter-AirBS cooperation or communication. The approach relies on a smart design of the network utility function and on a stochastic gradient ascent iteration that can be evaluated with information available in practical scenarios. To complement the theoretical convergence properties, a simulation study corroborates the effectiveness of the proposed scheme.

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“…For example, Zhao [22] proposed a brain-inspired decision-making spiking neural network (BDM-SNN) and applied it to decision-making tasks on UAVs. Park [23] addressed the analysis and deployment of the network infrastructure based on multiple Unmanned Air Vehicles in his new research. He modeled the generic dynamics of the network infrastructure, derived the network throughput of the infrastructure, and proposed a novel formation control algorithm that determines the location of the UAVs to maximize the efficiency of the network.…”

Section: Related Workmentioning

confidence: 99%

UAV Motion Strategies in Uncertain Dynamic Environments: A Path Planning Method Based on Q-Learning Strategy

et al. 2018

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A solution framework for UAV motion strategies in uncertain dynamic environments is constructed in this paper. Considering that the motion states of UAV might be influenced by some dynamic uncertainties, such as control strategies, flight environments, and any other bursting-out threats, we model the uncertain factors that might cause such influences to the path planning of the UAV, unified as an unobservable part of the system and take the acceleration together with the bank angle of the UAV as a control variable. Meanwhile, the cost function is chosen based on the tracking error, then the control instructions and flight path for UAV can be achieved. Then, the cost function can be optimized through Q-learning, and the best UAV action sequence for conflict avoidance under the moving threat environment can be obtained. According to Bellman’s optimization principle, the optimal action strategies can be obtained from the current confidence level. The method in this paper is more in line with the actual UAV path planning, since the generation of the path planning strategy at each moment takes into account the influence of the UAV control strategy on its motion at the next moment. The simulation results show that all the planning paths that are created according to the solution framework proposed in this paper have a very high tracking accuracy, and this method has a much shorter processing time as well as a shorter path it can create.

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Formation Control Algorithm of Multi-UAV-Based Network Infrastructure

Cited by 30 publications

References 37 publications

Synchronization in Time-Varying and Evolving Complex Networks

Synchronization in Time-Varying and Evolving Complex Networks

Non-Cooperative Aerial Base Station Placement via Stochastic Optimization

UAV Motion Strategies in Uncertain Dynamic Environments: A Path Planning Method Based on Q-Learning Strategy

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