Öz
Proje Numarası
TEKF.21.12
Kaynakça
- Okasha, M.; Kralev, J.; Islam, M. (2022). Design and Experimental Comparison of PID, LQR, and MPC Stabilizing Controllers for Parrot Mambo Mini-Drone, Aerospace, Vol. 9, No. 6, 298.
- Khodja, M. A.; Tadjine, M.; Boucherit, M. S.; Benzaoui, M. (2017). Experimental dynamics identification and control of a quadcopter, 2017 6th International Conference on Systems and Control, ICSC 2017, IEEE, 498–502.
- Sheta, A.; Braik, M.; Maddi, D. R.; Mahdy, A.; Aljahdali, S.; Turabieh, H. (2021). Optimization of PID controller to stabilize quadcopter movements using meta-heuristic search algorithms, Applied Sciences (Switzerland), Vol. 11, No. 14.
- Derrouaoui, S. H.; Bouzid, Y.; Guiatni, M. (2021). PSO Based Optimal Gain Scheduling Backstepping Flight Controller Design for a Transformable Quadrotor, Journal of Intelligent and Robotic Systems: Theory and Applications, Vol. 102, No. 3.
- Kapnopoulos, A.; Alexandridis, A. (2022). A cooperative particle swarm optimization approach for tuning an MPC-based quadrotor trajectory tracking scheme, Aerospace Science and Technology, Vol. 127, 107725.
- Hermouche, B.; Zennir, Y.; Kamsu Foguem, B. (2023). Influence of meta-heuristic algorithms on the optimization of quadrotor altitude PID controller, Journal of the Brazilian Society of Mechanical Sciences and Engineering, Vol. 45, No. 10, 1–19.
- Sahrir, N. H.; Basri, M. A. M. (2023). PSO–PID Controller for Quadcopter UAV: Index Performance Comparison, Arabian Journal for Science and Engineering, Vol. 48, No. 11, 15241–15255.
- Belge, E.; Altan, A.; Hacıoğlu, R. (2022). Metaheuristic Optimization-Based Path Planning and Tracking of Quadcopter for Payload Hold-Release Mission, Electronics (Switzerland), Vol. 11, No. 8.
- Gün, A. (2023). Attitude control of a quadrotor using PID controller based on differential evolution algorithm, Expert Systems with Applications, Vol. 229, No. PB, 120518.
- Alqudsi, Y.; Makaraci, M.; Kassem, A.; El-Bayoumi, G. (2023). A numerically-stable trajectory generation and optimization algorithm for autonomous quadrotor UAVs, Robotics and Autonomous Systems, Vol. 170, No. January 2021, 104532.
- Meraihi, Y.; Gabis, A. B.; Ramdane-Cherif, A.; Acheli, D. (2021). A comprehensive survey of Crow Search Algorithm and its applications, Artificial Intelligence Review, Vol. 54, No. 4, 2669–2716.
- Farzaneh, M. M.; Tavakolpour-Saleh, A. R. (2022). Stabilization of a quadrotor system using an optimal neural network controller, Journal of the Brazilian Society of Mechanical Sciences and Engineering, Vol. 44, No. 1, 1–12.
- Chang, W.-D.; Shih, S.-P. (2010). PID controller design of nonlinear systems using an improved particle swarm optimization approach, Communications in Nonlinear Science and Numerical Simulation, Vol. 15, No. 11, 3632–3639.
- Askarzadeh, A. (2016). A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Computers & structures, 169, 1-12.
- Kennedy, J., & Eberhart, R. (1995, November). Particle swarm optimization. In Proceedings of ICNN'95-international conference on neural networks (Vol. 4, pp. 1942-1948). ieee.
- Chopra, N., & Ansari, M. M. (2022). Golden jackal optimization: A novel nature-inspired optimizer for engineering applications. Expert Systems with Applications, 198, 116924.
- Chou, J. S., & Truong, D. N. (2021). A novel metaheuristic optimizer inspired by the behavior of jellyfish in the ocean. Applied Mathematics and Computation, 389, 125535.
- Kurnaz, M. K. (2023). Realization of image processing-based trajectory tracking algorithm on Parrot Mambo drone with MATLAB. Master's Thesis, https://tez.yok.gov.tr/.
Öz
Quadrotor, which is used in many fields and is still a challenge to control, has a complex kinematic and dynamic system, and its flight performance depends on many variables that need to be controlled simultaneously. In this study, the effective determination of PID parameters for altitude control of quadrotors, which presents a complex control problem, has been tested comparatively with innovative metaheuristic approaches. Among the strong metaheuristic algorithms, Crow Search Algorithm (CSA), Particle Swarm Optimization Algorithm (PSO), Golden Jackal Optimization Algorithm (GJO), and Jellyfish Search Algorithm (JSA) were comparatively analyzed for the determination of PID parameters. The parameters obtained with CSA caused the minimum steady-state error with the value of 6.9580e-04 in the closed-loop control system. A minimum overshoot was also obtained with the parameters optimized with CSA. When these results are evaluated, it can be said that CSA performs better than other altitude control algorithms, considering the quadrotor's stable and accurate positioning performance.
Anahtar Kelimeler
Crow Search Algorithm Golden Jackal Optimization Algorithm Jellyfish Search Algorithm Parameter Optimization Particle Swarm Optimization Algorithm quadrotor
Etik Beyan
This Study Does Not Need Ethics Committee Approval
Destekleyen Kurum
FUBAP
Proje Numarası
TEKF.21.12
Teşekkür
This study was derived from Master's thesis numbered 836713 of Muhammed Kıvanc KURNAZ under the supervision of Assoc. Dr. Gonca OZMEN KOCA. This study was also supported by Scientific Research Unit of Firat University (FUBAP) under the Grant Number TEKF.21.12. The authors thank to FUBAP for their supports.
Kaynakça
- Okasha, M.; Kralev, J.; Islam, M. (2022). Design and Experimental Comparison of PID, LQR, and MPC Stabilizing Controllers for Parrot Mambo Mini-Drone, Aerospace, Vol. 9, No. 6, 298.
- Khodja, M. A.; Tadjine, M.; Boucherit, M. S.; Benzaoui, M. (2017). Experimental dynamics identification and control of a quadcopter, 2017 6th International Conference on Systems and Control, ICSC 2017, IEEE, 498–502.
- Sheta, A.; Braik, M.; Maddi, D. R.; Mahdy, A.; Aljahdali, S.; Turabieh, H. (2021). Optimization of PID controller to stabilize quadcopter movements using meta-heuristic search algorithms, Applied Sciences (Switzerland), Vol. 11, No. 14.
- Derrouaoui, S. H.; Bouzid, Y.; Guiatni, M. (2021). PSO Based Optimal Gain Scheduling Backstepping Flight Controller Design for a Transformable Quadrotor, Journal of Intelligent and Robotic Systems: Theory and Applications, Vol. 102, No. 3.
- Kapnopoulos, A.; Alexandridis, A. (2022). A cooperative particle swarm optimization approach for tuning an MPC-based quadrotor trajectory tracking scheme, Aerospace Science and Technology, Vol. 127, 107725.
- Hermouche, B.; Zennir, Y.; Kamsu Foguem, B. (2023). Influence of meta-heuristic algorithms on the optimization of quadrotor altitude PID controller, Journal of the Brazilian Society of Mechanical Sciences and Engineering, Vol. 45, No. 10, 1–19.
- Sahrir, N. H.; Basri, M. A. M. (2023). PSO–PID Controller for Quadcopter UAV: Index Performance Comparison, Arabian Journal for Science and Engineering, Vol. 48, No. 11, 15241–15255.
- Belge, E.; Altan, A.; Hacıoğlu, R. (2022). Metaheuristic Optimization-Based Path Planning and Tracking of Quadcopter for Payload Hold-Release Mission, Electronics (Switzerland), Vol. 11, No. 8.
- Gün, A. (2023). Attitude control of a quadrotor using PID controller based on differential evolution algorithm, Expert Systems with Applications, Vol. 229, No. PB, 120518.
- Alqudsi, Y.; Makaraci, M.; Kassem, A.; El-Bayoumi, G. (2023). A numerically-stable trajectory generation and optimization algorithm for autonomous quadrotor UAVs, Robotics and Autonomous Systems, Vol. 170, No. January 2021, 104532.
- Meraihi, Y.; Gabis, A. B.; Ramdane-Cherif, A.; Acheli, D. (2021). A comprehensive survey of Crow Search Algorithm and its applications, Artificial Intelligence Review, Vol. 54, No. 4, 2669–2716.
- Farzaneh, M. M.; Tavakolpour-Saleh, A. R. (2022). Stabilization of a quadrotor system using an optimal neural network controller, Journal of the Brazilian Society of Mechanical Sciences and Engineering, Vol. 44, No. 1, 1–12.
- Chang, W.-D.; Shih, S.-P. (2010). PID controller design of nonlinear systems using an improved particle swarm optimization approach, Communications in Nonlinear Science and Numerical Simulation, Vol. 15, No. 11, 3632–3639.
- Askarzadeh, A. (2016). A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Computers & structures, 169, 1-12.
- Kennedy, J., & Eberhart, R. (1995, November). Particle swarm optimization. In Proceedings of ICNN'95-international conference on neural networks (Vol. 4, pp. 1942-1948). ieee.
- Chopra, N., & Ansari, M. M. (2022). Golden jackal optimization: A novel nature-inspired optimizer for engineering applications. Expert Systems with Applications, 198, 116924.
- Chou, J. S., & Truong, D. N. (2021). A novel metaheuristic optimizer inspired by the behavior of jellyfish in the ocean. Applied Mathematics and Computation, 389, 125535.
- Kurnaz, M. K. (2023). Realization of image processing-based trajectory tracking algorithm on Parrot Mambo drone with MATLAB. Master's Thesis, https://tez.yok.gov.tr/.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Elektrik Mühendisliği (Diğer) |
| Bölüm | Makaleler |
| Yazarlar | |
| Proje Numarası | TEKF.21.12 |
| Erken Görünüm Tarihi | 30 Haziran 2025 |
| Yayımlanma Tarihi | 30 Haziran 2025 |
| Gönderilme Tarihi | 14 Ekim 2024 |
| Kabul Tarihi | 24 Şubat 2025 |
| Yayımlandığı Sayı | Yıl 2025 Cilt: 9 Sayı: 1 |
Kaynak Göster
This work is licensed under CC BY-NC 4.0
