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Computational Intelligence and Neuroscience Volume 2019 ,2019-07-24
Quadrotor Identification through the Cooperative Particle Swarm Optimization-Cuckoo Search Approach
Research Article
Nada El gmili 1 Mostafa Mjahed 2 Abdeljalil El kari 1 Hassan Ayad 1
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DOI:10.1155/2019/8925165
Received 2019-05-19, accepted for publication 2019-07-02, Published 2019-07-02
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摘要

This paper explores the model parameters estimation of a quadrotor UAV by exploiting the cooperative particle swarm optimization-cuckoo search (PSO-CS). The PSO-CS regulates the convergence velocity benefiting from the capabilities of social thinking and local search in PSO and CS. To evaluate the efficiency of the proposed methods, it is regarded as important to apply these approaches for identifying the autonomous complex and nonlinear dynamics of the quadrotor. After defining the quadrotor dynamic modelling using Newton–Euler formalism, the quadrotor model’s parameters are extracted by using intelligent PSO, CS, PSO-CS, and the statistical least squares (LS) methods. Finally, simulation results prove that PSO and PSO-CS are more efficient in optimal tuning of parameters values for the quadrotor identification.

授权许可

Copyright © 2019 Nada El gmili et al. 2019
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

通讯作者

Nada El gmili.Applied Physics Department, Cadi Ayyad University, Marrakesh 40000, Morocco, uca.ma.elgmilinada@gmail.com

推荐引用方式

Nada El gmili,Mostafa Mjahed,Abdeljalil El kari,Hassan Ayad. Quadrotor Identification through the Cooperative Particle Swarm Optimization-Cuckoo Search Approach. Computational Intelligence and Neuroscience ,Vol.2019(2019)

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参考文献
[1] A. S. Joshi, O. Kulkarni, G. M. Kakandikar, V. M. Nandedkar. et al.(2017). Cuckoo search optimization- a review. Materials Today: Proceedings.4(8):7262-7269. DOI: 10.5772/45710.
[2] J. Gośliński, S. Gardecki, W. Giernacki. (2015). An efficient PSO-based method for an identification of a Quadrotor model parameters. Progress in Automation, Robotics and Measuring Techniques:95-104. DOI: 10.5772/45710.
[3] A. P. Patwardhan, R. Patidar, N. V. George. (2014). On a cuckoo search optimization approach towards feedback system identification. Digital Signal Processing.32:156-163. DOI: 10.5772/45710.
[4] N. E. Gmili, M. Mjahed, A. E. Kari, H. Ayad. et al.(2018). Intelligent PSO-based PDs/PIDs controllers for an unmanned quadrotor. International Journal of Intelligent Engineering Informatics.6(6):548-568. DOI: 10.5772/45710.
[5] N. H. Abbas, A. R. Sami. (2018). Tuning of PID controllers for quadcopter system using cultural exchange imperialist competitive algorithm. Journal of Engineering.24(2):80-99. DOI: 10.5772/45710.
[6] N. El Gmili, M. Mjahed, A. El Kari, H. Ayad. et al.(2019). Particle swarm optimization and cuckoo search-based approaches for quadrotor control and trajectory tracking. Applied Sciences.9(8):1719. DOI: 10.5772/45710.
[7] W. Guo, J. Li, G. Chen. (2014). A PSO-optimized real-time fault-tolerant task allocation algorithm in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems.26(12):3236-3249. DOI: 10.5772/45710.
[8] A. Gotmare, R. Patidar, N. V. George. (2015). Nonlinear system identification using a cuckoo search optimized adaptive Hammerstein model. Expert Systems with Applications.42(5):2538-2546. DOI: 10.5772/45710.
[9] W. Guo, G. Liu, G. Chen, S. Peng. et al.(2014). A hybrid multi-objective PSO algorithm with local search strategy for VLSI partitioning. Frontiers of Computer Science.8(2):203-216. DOI: 10.5772/45710.
[10] A. M. Kamoona, J. C. Patra, A. Stojcevski. An enhanced cuckoo search algorithm for solving optimization problems. :1-6. DOI: 10.5772/45710.
[11] A. Noordin, M. A. Mohd Basri, Z. Mohamed, A. F. Zainal Abidin. et al.(2017). Modelling and PSO fine-tuned PID control of quadrotor UAV. International Journal on Advanced Science, Engineering and Information Technology.7(4):1367-1373. DOI: 10.5772/45710.
[12] V. Verdult, M. Verhaegen. (2007). Filtering and System Identification: A Least Squares Approach. DOI: 10.5772/45710.
[13] R. Pupeikis. (2011). On recursive parametric identification of Wiener systems. Information Technology and Control.40(1):21-28. DOI: 10.5772/45710.
[14] F. Marini, B. Walczak. (2015). Particle swarm optimization (PSO). A tutorial. Chemometrics and Intelligent Laboratory Systems.149:153-165. DOI: 10.5772/45710.
[15] Y. Naidoo, R. Stopforth, G. Bright. (2011). Quad-rotor unmanned aerial vehicle helicopter modelling & control. International Journal of Advanced Robotic Systems.8(4):45. DOI: 10.5772/45710.
[16] J. Kennedy, R. C. Eberhart. Particle swarm optimization. :1942-1948. DOI: 10.5772/45710.
[17] N. El Gmili, M. Mjahed, A. el Kari, H. Ayad. et al.(2017). An improved particle swarm optimization (IPSO) approach for identification and control of stable and unstable systems. International Review of Automatic Control (IREACO).10(3):229-239. DOI: 10.5772/45710.
[18] G. Liu, X. Huang, W. Guo. (2014). Multilayer obstacle-avoiding X-architecture Steiner minimal tree construction based on particle swarm optimization. IEEE Transactions on Cybernetics.45(5):1003-1016. DOI: 10.5772/45710.
[19] M. Mjahed. Optimization of classification tasks by using genetic algorithms. :1-4. DOI: 10.5772/45710.
[20] G. Liu, W. Guo, R. Li, Y. Niu. et al.(2015). XGRouter: high-quality global router in X-architecture with particle swarm optimization. Frontiers of Computer Science.9(4):576-594. DOI: 10.5772/45710.
[21] B. Samir. (2007). Design and control of quadrotors with application to autonomous flying. . DOI: 10.5772/45710.
[22] M. Mjahed. PID controller design using genetic algorithm technique. :1-4. DOI: 10.5772/45710.
[23] A. D. Jeraldin. (2015). Adaptive Particle Swarm Optimization based system identification and internal model sliding mode controller for mass flow system. Journal of Control Engineering and Applied Informatics.17(4):3-13. DOI: 10.5772/45710.
[24] D. E. Goldberg, J. H. Holland. (1988). Genetic algorithms and machine learning. Machine Learning.3(2-3):95-99. DOI: 10.5772/45710.
[25] Z. Cui, B. Sun, G. Wang, Y. Xue. et al.(2017). A novel oriented cuckoo search algorithm to improve DV-Hop performance for cyber-physical systems. Journal of Parallel and Distributed Computing.103:42-52. DOI: 10.5772/45710.
[26] Z. Cui, M. Zhang, H. Wang. (2019). A hybrid many-objective cuckoo search algorithm. Soft Computing:1-17. DOI: 10.5772/45710.
[27] R. Lozano. (2013). Unmanned Aerial Vehicles: Embedded Control. DOI: 10.5772/45710.
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