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Ateş Böceği Algoritması ile Quadrotor Pozisyon Kontrolü

Year 2022, Volume: 9 Issue: 2, 554 - 566, 31.05.2022
https://doi.org/10.31202/ecjse.975718

Abstract

Quadrotor, mükemmel çevikliğe, dört pervaneye ve altı serbestlik derecesine sahip, VTOL özellikli bir insansız hava aracıdır. Basit yapıları ve düşük maliyetleri nedeniyle son yıllarda oldukça ilgi görmektedirler. Görünür basitliğine rağmen, doğrusal olmayışı ve bağlantılı dinamikleri kontrolü zorlaştırır. Basit yapısı nedeniyle PD kontrolü, quadrotorlarda yaygın olarak kullanılmaktadır. Bu makalede, Ateş Böceği Algoritması ve Genetik Algoritma kullanılarak hesaplanan parametreleriyle dört rotorlu bir quadratorun, yükseklik ve konum stabilizasyonu için bir PD denetleyicisi önerilmiştir. Yalnızca hedef pozisyonun hassas bir şekilde konumlandırılmasını sağlamak için değil, aynı zamanda hareketin yerleşme süresini de iyileştirmek için bir amaç fonksiyonu önerilmiştir. Önerilen yöntemin performans doğrulaması için dinlenme konumundan hedef konuma bir test yolu incelenir. Elde edilen bulgular, pozisyon stabilizasyonunun kısa sürede gerçekleştirilebileceğini ve Genetik algoritma tarafından belirlenen ayarlara kıyasla yerleşme süresinin önemli ölçüde azaldığını göstermektedir. Ataş Böceği Algoritması yöntemi ile belirlenen PD kontrolör parametreleri, Genetik Algoritma tarafından seçilenleri geride bırakmaktadır.

References

  • [1]. Bouabdallah, S. and Siegwart, R., Backstepping and Sliding-mode Techniques Applied to an Indoor Micro Quadrotor, in Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2005, 2247-2252.
  • [2]. Dydek, Z.T., Annaswamy, A. M. and Lavretsky, E., Adaptive Control of Quadrotor UAVs: A Design Trade Study with Flight Evaluations, in IEEE Transactions on Control Systems Technology, 2013, 21(4), 1400-1406.
  • [3]. Tayebi, A. and McGilvray, S., Attitude stabilization of a VTOL quadrotor aircraft, in IEEE Transactions on Control Systems Technology, 2006, 14 (3), 562-571.
  • [4]. Boubertakh, H., Bencharef, S. and Labiod, S., PSO-based PID control design for the stabilization of a quadrotor, 3rd International Conference on Systems and Control, Algiers, 2013, 514-517.
  • [5]. Liu, X., Zhao, D. and Wu, Y., Application of improved PSO in PID parameter optimization of quadrotor, 12th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), Chengdu, 2015, 443-447.
  • [6]. Mac, T. T., Copot, C., Duc, T. T., and De Keyser, R., AR Drone UAV control parameters tuning based on particle swarm optimization algorithm, 2016 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR), Cluj-Napoca, 2016, 1-6.
  • [7]. Chiou, J. S., Tran, H. K., Shieh, M. Y., Nguyen, T. N., Particle swarm optimization algorithm reinforced fuzzy proportional–integral–derivative for a quadrotor attitude control, Advances in Mechanical Engineering, 2016, 8(9).
  • [8]. Rezazadeh, S., Ardestani, M. A. and Sadeghi, P. S., Optimal attitude control of a quadrotor UAV using Adaptive Neuro-Fuzzy Inference System (ANFIS), The 3rd International Conference on Control, Instrumentation, and Automation, Tehran, 2013, 219-223.
  • [9]. Noshahri, H. and Kharrati, H., PID controller design for unmanned aerial vehicle using genetic algorithm, 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE), Istanbul, 2014, 213-217.
  • [10]. Bencharef, S. and Boubertakh, H., Optimal tuning of a PD control by bat algorithm to stabilize a quadrotor, 2016 8th International Conference on Modelling, Identification and Control (ICMIC), Algiers, 2016, 938-942.
  • [11]. Pedro, J. O., Dangor, M. and Kala, P. J., Differential evolution-based PID control of a quadrotor system for hovering application, 2016 IEEE Congress on Evolutionary Computation (CEC), Vancouver, BC, 2016, 2791-2798.
  • [12]. Reyad, M., Arafa, M. and Sallam, E. A., An optimal PID controller for a qaudrotor system based on DE algorithm, 2016 11th International Conference on Computer Engineering & Systems (ICCES), Cairo, 2016, 444-451.
  • [13]. Hasseni, SEI., Abdou, L. & Glida, HE. Parameters tuning of a quadrotor PID controllers by using nature-inspired algorithms. Evol. Intel., 2021, 14, 61–73.
  • [14]. Yang, X. S., Nature-Inspired Metaheuristic Algorithms, Luniver Press, 2nd ed., 2010, 81-91.
  • [15]. ElKholy, Heba talla Mohamed Nabil. 2014. Dynamic Modeling and Control of a Quadrotor Using Linear and Nonlinear Approaches. Thesis, America University in Cairo.
  • [16]. Sabatino, F., Quadrotor control: modeling, nonlinear control design, and simulation, Master’s Degree Project, KTH Electrical Engineering, Stockholm, June 2015.
  • [17]. Selby, W. C., Autonomous Navigation and Tracking of Dynamic Surface Targets On-board a Computationally Impoverished Aerial Vehicle, Master’s Degree Project, Massachusetts Institute of Technology, June 2011.

Position Control of Quadrotor using Firefly Algorithm

Year 2022, Volume: 9 Issue: 2, 554 - 566, 31.05.2022
https://doi.org/10.31202/ecjse.975718

Abstract

The quadrotor is a VTOL-capable unmanned aerial vehicle with excellent agility, four propellers, and six degrees of freedom. Because of their simple structure and low cost, they have attracted a lot of interest in recent years. Despite its apparent simplicity, its nonlinearities and linked dynamics make control difficult. Because of its simple nature, PD control is extensively utilized in quadrotors. A PD controller for quadrotor altitude and position stabilization is proposed in this paper, with its parameters calculated using the Firefly Algorithm and Genetic Algorithm. An objective function is developed to offer not only precise positioning of the target position, but also to enhance the motion's settling time. A test path from the rest position to the target position is examined for performance verification of the suggested method. The obtained findings show that position stabilization may be accomplished in a short amount of time, and the settling time is significantly reduced when compared to specified settings by Genetic Algorithm. The PD controller settings determined via the Firefly Algorithm optimization method surpass the ones chosen by Genetic Algorithm with a substantial margin.

References

  • [1]. Bouabdallah, S. and Siegwart, R., Backstepping and Sliding-mode Techniques Applied to an Indoor Micro Quadrotor, in Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2005, 2247-2252.
  • [2]. Dydek, Z.T., Annaswamy, A. M. and Lavretsky, E., Adaptive Control of Quadrotor UAVs: A Design Trade Study with Flight Evaluations, in IEEE Transactions on Control Systems Technology, 2013, 21(4), 1400-1406.
  • [3]. Tayebi, A. and McGilvray, S., Attitude stabilization of a VTOL quadrotor aircraft, in IEEE Transactions on Control Systems Technology, 2006, 14 (3), 562-571.
  • [4]. Boubertakh, H., Bencharef, S. and Labiod, S., PSO-based PID control design for the stabilization of a quadrotor, 3rd International Conference on Systems and Control, Algiers, 2013, 514-517.
  • [5]. Liu, X., Zhao, D. and Wu, Y., Application of improved PSO in PID parameter optimization of quadrotor, 12th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), Chengdu, 2015, 443-447.
  • [6]. Mac, T. T., Copot, C., Duc, T. T., and De Keyser, R., AR Drone UAV control parameters tuning based on particle swarm optimization algorithm, 2016 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR), Cluj-Napoca, 2016, 1-6.
  • [7]. Chiou, J. S., Tran, H. K., Shieh, M. Y., Nguyen, T. N., Particle swarm optimization algorithm reinforced fuzzy proportional–integral–derivative for a quadrotor attitude control, Advances in Mechanical Engineering, 2016, 8(9).
  • [8]. Rezazadeh, S., Ardestani, M. A. and Sadeghi, P. S., Optimal attitude control of a quadrotor UAV using Adaptive Neuro-Fuzzy Inference System (ANFIS), The 3rd International Conference on Control, Instrumentation, and Automation, Tehran, 2013, 219-223.
  • [9]. Noshahri, H. and Kharrati, H., PID controller design for unmanned aerial vehicle using genetic algorithm, 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE), Istanbul, 2014, 213-217.
  • [10]. Bencharef, S. and Boubertakh, H., Optimal tuning of a PD control by bat algorithm to stabilize a quadrotor, 2016 8th International Conference on Modelling, Identification and Control (ICMIC), Algiers, 2016, 938-942.
  • [11]. Pedro, J. O., Dangor, M. and Kala, P. J., Differential evolution-based PID control of a quadrotor system for hovering application, 2016 IEEE Congress on Evolutionary Computation (CEC), Vancouver, BC, 2016, 2791-2798.
  • [12]. Reyad, M., Arafa, M. and Sallam, E. A., An optimal PID controller for a qaudrotor system based on DE algorithm, 2016 11th International Conference on Computer Engineering & Systems (ICCES), Cairo, 2016, 444-451.
  • [13]. Hasseni, SEI., Abdou, L. & Glida, HE. Parameters tuning of a quadrotor PID controllers by using nature-inspired algorithms. Evol. Intel., 2021, 14, 61–73.
  • [14]. Yang, X. S., Nature-Inspired Metaheuristic Algorithms, Luniver Press, 2nd ed., 2010, 81-91.
  • [15]. ElKholy, Heba talla Mohamed Nabil. 2014. Dynamic Modeling and Control of a Quadrotor Using Linear and Nonlinear Approaches. Thesis, America University in Cairo.
  • [16]. Sabatino, F., Quadrotor control: modeling, nonlinear control design, and simulation, Master’s Degree Project, KTH Electrical Engineering, Stockholm, June 2015.
  • [17]. Selby, W. C., Autonomous Navigation and Tracking of Dynamic Surface Targets On-board a Computationally Impoverished Aerial Vehicle, Master’s Degree Project, Massachusetts Institute of Technology, June 2011.
There are 17 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Makaleler
Authors

Büşra Keskin 0000-0003-4378-7758

Kemal Keskin 0000-0002-3969-2396

Publication Date May 31, 2022
Submission Date July 28, 2021
Acceptance Date December 24, 2021
Published in Issue Year 2022 Volume: 9 Issue: 2

Cite

IEEE B. Keskin and K. Keskin, “Position Control of Quadrotor using Firefly Algorithm”, ECJSE, vol. 9, no. 2, pp. 554–566, 2022, doi: 10.31202/ecjse.975718.