scholarly journals Longitudinal parameter identification of a small unmanned aerial vehicle based on modified particle swarm optimization

2015 ◽  
Vol 28 (3) ◽  
pp. 865-873 ◽  
Author(s):  
Tieying Jiang ◽  
Jie Li ◽  
Kewei Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Parameter Identification ◽  
Unmanned Aerial Vehicle ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Aerial Vehicle

scholarly journals Improved Particle Swarm Optimization of Three-Dimensional Path Planning for Fixed Wing Unmanned Aerial Vehicle

2019 ◽  
Vol 35 (2) ◽  
Author(s):  
Giang Thi - Huong Dang ◽  
Quang - Huy Vuong ◽  
Minh Hoang Ha ◽  
Minh - Trien Pham
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Unmanned Aerial Vehicle ◽  
Particle Swarm ◽  
Adaptive Mutation ◽  
Planning Problem ◽  
Swarm Optimization ◽  
3D Environment ◽  
Aerial Vehicle ◽  
Improved Pso

Path planning for Unmanned Aerial Vehicle (UAV) targets at generating an optimal global path to the target, avoiding collisions and optimizing the given cost function under constraints. In this paper, the path planning problem for UAV in pre-known 3D environment is presented. Particle Swarm Optimization (PSO) was proved the efficiency for various problems. PSO has high convergence speed yet with its major drawback of premature convergence when solving large-scale optimization problems. In this paper, the improved PSO with adaptive mutation to overcome its drawback in order to applied PSO the UAV path planning in real 3D environment which composed of mountains and constraints. The effectiveness of the proposed PSO algorithm is compared to Genetic Algorithm, standard PSO and other improved PSO using 3D map of Daklak, Dakrong and Langco Beach. The results have shown the potential for applying proposed algorithm in optimizing the 3D UAV path planning. Keywords: UAV, Path planning, PSO, Optimization.

scholarly journals Particle Swarm Optimization for Interference-Limited Unmanned Aerial Vehicle-Assisted Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 174342-174352
Author(s):  
Wentao Liu ◽  
Guanchong Niu ◽  
Qi Cao ◽  
Man-On Pun ◽  
Junting Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Unmanned Aerial Vehicle ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Aerial Vehicle

scholarly journals PERANCANGAN SISTEM KONTROL NAVIGASI BEARING PADA QUADCOPTER DENGAN METODE PID (PROPORTIONAL, INTEGRAL, DERIVATIVE) SELF TUNING PSO (PARTICLE SWARM OPTIMIZATION)

TRANSIENT ◽  
2017 ◽  
Vol 6 (3) ◽  
pp. 323
Author(s):  
Muhammad Surya Sulila ◽  
Sumardi Sumardi ◽  
Munawar Agus Riyadi
Keyword(s):  
Particle Swarm Optimization ◽  
Global Position System ◽  
Unmanned Aerial Vehicle ◽  
Particle Swarm ◽  
Settling Time ◽  
Proportional Integral Derivative ◽  
Swarm Optimization ◽  
Proportional Integral ◽  
Self Tuning ◽  
Aerial Vehicle

Unmanned Aerial Vehicle (UAV) adalah pesawat tanpa awak yang dapat dikendalikan secara manual ataupun otomatis dari jarak jauh. Sistem navigasi UAV quadcopter salah satunya adalah membuat sistem kontrol quadcopter agar dapat stabil menghadap ke arah koordinat yang dituju dengan mengatur sudut putar sumbu vertikal (yaw) atau disebut navigasi bearing sehingga pada Penelitian ini dirancang sistem kontrol Proportional Integral Derivative self tuning Particle Swarm Optimization. Perancangan sistem navigasi bearing digunakan input berupa Global Position System untuk mengetahui koordinat quadcopter, sedangkan sensor kompas HMC5883L digunakan untuk mengetahui kondisi aktual sudut arah hadap quadcopter. Berdasarkan hasil pengujian respon sistem quadcopter, untuk dapat mengarah ke koordinat yang dituju dengan koordinat quadcopter tetap, settling time dicapai pada detik ke 6,4 dan error setelah settling time sebesar 5,4⁰. Berdasarkan pengujian dengan perubahan koordinat, didapatkan error rata-rata sebesar 7,9⁰. Berdasarkan pengujian dengan diberi gangguan didapatkan error offset rata-rata sebesar 1,89⁰ dan mencapai settling time pada detik ke 4,1. Batasan nilai self tuning PSO yang terbaik didapat pada nilai Kp = 0,15 sampai 0,3, Ki = 0,06 sampai 0,6, dan Kd = 0,005 sampai Kd = 0,1. Nilai koefisien PSO yang digunakan adalah C1 = 1,5,  C2 = 2 dan bobot inersia dari 0,7 sampai 1,2.

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