scholarly journals UAV-Based Collaborative Electronic Reconnaissance Network for 6G

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Fucheng Yang ◽  
Jie Song ◽  
Wei Xiong ◽  
Xutao Cui
Keyword(s):  
Topology Optimization ◽  
Unmanned Aerial Vehicle ◽  
Network Topology ◽  
Cooperative Network ◽  
Performance Simulation ◽  
Aerial Vehicle ◽  
Simulation Results ◽  
Novel Strategy ◽  
Network Topology Optimization ◽  
Command Center

In unmanned aerial vehicle (UAV) collaborative electronic reconnaissance network, single UAV is always restricted by flyability and sensing capacity; hence, a cooperative network is built to realize the electronic reconnaissance. In this paper, a three-level electronic reconnaissance network is proposed, including the radiation target, UAV-based electronic reconnaissance equipment, and the command center. Each of the UAVs is capable of monitoring several radiation targets at the same time. Since the topology of the UAV network influences the effect of electronic reconnaissance, in this contribution, optimization is achieved based on the improvement of radiation coverage. If there is no radiation target within the sensing scope, the corresponding UAV will remove according to our novel strategy. Iterate operations are carried out for the relative optimum performance. Simulation results show that the UAV network topology optimization is capable of improving the coverage of radiation targets effectively.

scholarly journals Network topology optimization by turning non-scale-free networks into scale-free networks using nonlinear preferential rewiring method

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771878447 ◽  
Author(s):  
Feng Su ◽  
Peijiang Yuan ◽  
Yuanwei Liu ◽  
Shuangqian Cao
Keyword(s):  
Topology Optimization ◽  
Network Topology ◽  
Random Networks ◽  
Practical Application ◽  
Performance Simulation ◽  
Scale Free ◽  
Scale Free Networks ◽  
Network Topologies ◽  
Network Topology Optimization ◽  
Rigorous Mathematical Model

In practical application, the generation and evolution of many real networks always do not follow rigorous mathematical model, making network topology optimization a great challenge in the field of complex networks. In this research, we optimize the topology of non-scale-free networks by turning it into scale-free networks using a nonlinear preferential rewiring method. For different kinds of original networks generated by Watts and Strogatz model, we systematically demonstrate the optimization process and the modified networks to verify the performance of nonlinear preferential rewiring. We conduct further researches to explore the effect of nonlinear preferential rewiring’s parameters on performance. Simulation results show that various non-scale-free networks with different network topologies generated by WS model, including random networks and various networks between regular and random, are turned into scale-free networks perfectly by nonlinear preferential rewiring method.

Toward Online Control of Local Bifurcation in Power Systems via Network Topology Optimization

2015 ◽  
Vol 25 (12) ◽  
pp. 1550167
Author(s):  
Lei Wang ◽  
Hsiao-Dong Chiang
Keyword(s):  
Topology Optimization ◽  
Power Systems ◽  
Transmission Line ◽  
Network Topology ◽  
Online Control ◽  
Power Grids ◽  
Local Bifurcations ◽  
Network Topology Optimization ◽  
Online Methods ◽  
The Impact

This paper presents online methods for controlling local bifurcations of power grids with the goal of increasing bifurcation values (i.e. increasing load margins) via network topology optimization, a low-cost control. In other words, this paper presents online methods for increasing power transfer capability subject to static stability limit via switching transmission line out/in (i.e. disconnecting a transmission line or connecting a transmission line). To illustrate the impact of network topology on local bifurcations, two common local bifurcations, i.e. saddle-node bifurcation and structure-induced bifurcation on small power grids with different network topologies are shown. A three-stage online control methodology of local bifurcations via network topology optimization is presented to delay local bifurcations of power grids. Online methods must meet the challenging requirements of online applications such as the speed requirement (in the order of minutes), accuracy requirement and robustness requirement. The effectiveness of the three-stage methodology for online applications is demonstrated on the IEEE 118-bus and a 1648-bus practical power systems.

scholarly journals PERANCANGAN AUTOPILOT LATERAL-DIREKSIONAL PESAWAT NIRAWAK LSU-05 (THE DESIGN OF THE LATERAL-DIRECTIONAL AUTOPILOT FOR THE LSU-05 UNMANNED AERIAL VEHICLE)

2018 ◽  
Vol 15 (2) ◽  
pp. 93 ◽  
Author(s):  
Muhammad Fajar ◽  
Ony Arifianto
Keyword(s):  
State Space ◽  
Linear Model ◽  
Unmanned Aerial Vehicle ◽  
Pid Controller ◽  
Settling Time ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Directional Motion ◽  
Aerial Vehicle ◽  
Simulation Results

The autopilot on the aircraft is developed based on the mode of motion of the aircraft i.e. longitudinal and lateral-directional motion. In this paper, an autopilot is designed in lateral-directional mode for LSU-05 aircraft. The autopilot is designed at a range of aircraft operating speeds of 15 m/s, 20 m/s, 25 m/s, and 30 m/s at 1000 m altitude. Designed autopilots are Roll Attitude Hold, Heading Hold and Waypoint Following. Autopilot is designed based on linear model in the form of state-space. The controller used is a Proportional-Integral-Derivative (PID) controller. Simulation results show the value of overshoot / undershoot does not exceed 5% and settling time is less than 30 second if given step command. Abstrak Autopilot pada pesawat dikembangkan berdasarkan pada modus gerak pesawat yaitu modus gerak longitudinal dan lateral-directional. Pada makalah ini, dirancang autopilot pada modus gerak lateral-directional untuk pesawat LSU-05. Autopilot dirancang pada range kecepatan operasi pesawat yaitu 15 m/dtk, 20 m/dtk, 25 m/dtk, dan 30 m/dtk dengan ketinggian 1000 m. Autopilot yang dirancang adalah Roll Attitude Hold, Heading Hold dan Waypoint Following. Autopilot dirancang berdasarkan model linier dalam bentuk state-space. Pengendali yang digunakan adalah pengendali Proportional-Integral-Derivative (PID). Hasil simulasi menunjukan nilai overshoot/undershoot tidak melebihi 5% dan settling time kurang dari 30 detik jika diberikan perintah step.

scholarly journals Fast Nondominated Sorting Genetic Algorithm II with Lévy Distribution for Network Topology Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Maoqing Zhang ◽  
Lei Wang ◽  
Zhihua Cui ◽  
Jiangshan Liu ◽  
Dong Du ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Topology Optimization ◽  
Network Topology ◽  
Optimization Problems ◽  
Selection Strategy ◽  
Nsga Ii ◽  
Comparison Results ◽  
Practical Engineering ◽  
Network Topology Optimization ◽  
Nondominated Sorting

Fast nondominated sorting genetic algorithm II (NSGA-II) is a classical method for multiobjective optimization problems and has exhibited outstanding performance in many practical engineering problems. However, the tournament selection strategy used for the reproduction in NSGA-II may generate a large amount of repetitive individuals, resulting in the decrease of population diversity. To alleviate this issue, Lévy distribution, which is famous for excellent search ability in the cuckoo search algorithm, is incorporated into NSGA-II. To verify the proposed algorithm, this paper employs three different test sets, including ZDT, DTLZ, and MaF test suits. Experimental results demonstrate that the proposed algorithm is more promising compared with the state-of-the-art algorithms. Parameter sensitivity analysis further confirms the robustness of the proposed algorithm. In addition, a two-objective network topology optimization model is then used to further verify the proposed algorithm. The practical comparison results demonstrate that the proposed algorithm is more effective in dealing with practical engineering optimization problems.

Elastic Formation Keeping Control of Unmanned Aerial Vehicle Adapting to Flight Speed

2013 ◽  
Vol 367 ◽  
pp. 411-416 ◽  
Author(s):  
Guang Yan Xu ◽  
Yi Bo Shi
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Relative Position ◽  
State Feedback ◽  
Feedback Controller ◽  
Flight Speed ◽  
Dynamic Equations ◽  
Aerial Vehicle ◽  
Distance Vector ◽  
Simulation Results ◽  
Formation Keeping

For an Unmanned Aerial Vehicle (UAV) formation in leader-follower mode, considering the relative position relationship between neighbor vehicles in the formation, an elastic distance vector is proposed. The dynamic equations of a flight speed adaptive UAV formation are established using the elastic distance vector we proposed. The state feedback controller is designed. Simulation results show that the controller can be used to control the follower vehicles to follow the leader vehicle maneuvering effectively and keep the desired formation well, most importantly, the relative distance between neighbor vehicles in the formation is adapted to the changes of flight speed.

Swarming Algorithm for Unmanned Aerial Vehicle (UAV) Quadrotors – Swarm Behavior for Aggregation, Foraging, Formation, and Tracking –

2014 ◽  
Vol 18 (5) ◽  
pp. 745-751 ◽  
Author(s):  
Argel A. Bandala ◽  
◽  
Elmer P. Dadios ◽  
Ryan Rhay P. Vicerra ◽  
Laurence A. Gan Lim
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Computer Simulations ◽  
Group Performance ◽  
Robotic System ◽  
Robot Swarms ◽  
Average Accuracy ◽  
Swarm Behavior ◽  
Aerial Vehicle ◽  
Simulation Results ◽  
Robotic Agent

This paper presents the fusion of swarm behavior in multi robotic system specifically the quadrotors unmanned aerial vehicle (QUAV) operations. This study directed on using robot swarms because of its key feature of decentralized processing amongst its member. This characteristic leads to advantages of robot operations because an individual robot failure will not affect the group performance. The algorithm emulating the animal or insect swarm behaviors is presented in this paper and implemented into an artificial robotic agent (QUAV) in computer simulations. The simulation results concluded that for increasing number of QUAV the aggregation accuracy increases with an accuracy of 90.62%. The experiment for foraging revealed that the number of QUAV does not affect the accuracy of the swarm instead the iterations needed are greatly improved with an average of 160.53 iterations from 50 to 500 QUAV. For swarm tracking, the average accuracy is 89.23%. The accuracy of the swarm formation is 84.65%. These results clearly defined that the swarm system is accurate enough to perform the tasks and robust in any QUAV number.

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