Multi-Parameter Optimization of Sliding-Mode Controller for Quadcopter Application

Abstract

For many years, quadcopters are quite popular in the academic field because of its structural simplicity. However, this property comes out the problem of designing an effective controller. Designing a controller for quadcopter is rather complicated because tuning of the controller parameters of multi-rotor structure to achieve a desired performance for agility, flying efficiency and immediate reaction is a challenging problem. To deal with such a difficulty, Ant Colony Optimization (ACO), Invasive Weed Optimization (IWO) and Firefly Optimization (FO) algorithms are used to obtain optimal parameters of Sliding Mode Controller (SMC). SMC is used for both attitude and position control of the quadcopter. By taking into consideration all six variables with different number of parameters (total number of parameters to be optimized are nineteen). This makes it a complicated tuning problem. In this numerical study, performance results of optimization algorithms are compared with respect to convergence rate and cost function.

Keywords

• quadcopter,ant colony optimization,firefly optimization,invasive weed optimization,sliding mode control

Quadcopter Uygulamaları için Kayma Kipli Kontrolörün Çok Parametreli Optimizasyonu

Abstract

Uzun yıllardan beri, quadcopterler, yapısal sadeliği nedeniyle akademik alanda oldukça popülerdir. Ancak, bu özellik etkili bir denetleyici tasarlama sorununu ortaya çıkarmaktadır. Quadcopter için bir kontrolör tasarlamak oldukça karmaşıktır çünkü çok rotorlu yapının kontrolör parametrelerinin ayarlanması, çeviklik, uçuş verimliliği ve anlık reaksiyon için istenen performansı sağlamak bakımından zor bir problemdir. Böyle bir zorlukla başa çıkmak için, Karınca Koloni Optimizasyonu (ACO), Yayılmacı Yosun Optimizasyonu (IWO) ve Ateş Böceği Optimizasyonu (FO) algoritmaları, kayma kipli kontrolörün (SMC) optimal parametrelerini elde etmek için uygulanmıştır. SMC, quadcopter'in hem durumsal hem de pozisyon kontrolü için çift katmanlı olarak tasarlanıp kullanılmıştır. Farklı sayıda parametreye sahip altı değişkenin hesaba katılmasıyla optimize edilecek toplam parametre sayısı on dokuz olmuştur. Bu da karmaşık bir ince ayarlama problemini ortaya çıkartmaktadır. Bu sayısal çalışmada, optimizasyon algoritmalarının performans sonuçları, yakınsama oranı ve maliyet fonksiyonuna göre karşılaştırmalı olarak sunulmuştur.

Keywords

• quadcopter,karınca kolonisi optimizasyonu,ateş böceği optimizasyonu,yayılmacı yusun optimizasyonu,kayma kipli kontrol

References

1. A. Baldini, L. Ciabattoni, R. Felicetti, F. Ferracuti and A. Monteriù. (2017). Particle Swarm Optimization Based Sliding Mode Control Design: Application to a Quadrotor Vehicle. In Applications of Sliding Mode Control in Science and Engineering (pp. 143-169). Springer International Publishing.Abraham, A., Grosan, C., &
2. Ramos, V. (Eds.). (2006). Stigmergic Optimization (Vol. 31). Berlin: Springer-Verlag Berlin Heidelberg. doi:10.1007/978-3-540-34690-6
3. Bo Xing, Wen-Jing Gao. (2014). Innovative Computational Intelligence A Rough Guide to 134 Clever Algorithms. Switzerland: Springer International Publishing.
4. Bouabdallah, S. (2007, February). PhD. Thesis. Design and control of quadrotors with application to autonomous flying. Lausanne, Sweden: EPFL.
5. Dorigo, M. (1992). PhD. Thesis. Optimization, learning and natural algorithms. Milano, Italy: Politecnico di Milano.
6. F. Yacef, O. Bouhali, M. Hamerlain, and A. Rezoug. (October 29-31, 2013). PSO optimization of Integral Backstepping Controller for Quadrotor Attitude Stabilization. Proceedings of the 3rd International Conference on Systems and Control. Algiers, Algeria.
7. Gustavsson, K. (2015). Master Thesis. UAV Pose Estimation using Sensor Fusion of Inertial, Sonar and Satellite Signals. Uppsala: Uppsala University.
8. I. C. Dikmen, A. Arisoy and H. Temeltas. (2009). Attitude control of a quadrotor. 4th International Conference on Recent Advances in Space Technologies, (pp. 722-727). Istanbul.

9. I. De Falco, A. Della Cioppa, D. Maisto, U. Scafuri, E. Tarantino. (2012). Biological invasion–inspired migration in distributed evolutionary algorithms. Information Sciences, 207, 50-65.
10. Lebao Li, Lingling Sun and Jie Jin. (2015). Survey of advances in control algorithms of quadrotor unmanned aerial vehicle. IEEE 16th International Conference on Communication Technology (ICCT), (pp. 107-111). Hangzhou.
11. Marco Dorigo, Mauro Birattari, Thomas Stützle. (2006, December). Ant Colony Optimization. IEEE Computational Intelligence Magazine, 1(4), 28-39.
12. Mehrabian AR, Lucas C. (2006;). A novel numerical optimization algorithm inspired from weed colonization. Ecological Informatics, 1(4), 355–366.
13. Nanako Shigesada, Kohkichi Kawasaki. (1997). Biological Invasions: Theory and Practice. Tokyo: Oxford University Press.
14. P. D. Sheth and A. J. Umbarkar. (2015). Constrained Optimization Problems Solving Using Evolutionary Algorithms: A Review. International Conference on Computational Intelligence and Communication Networks (CICN), (pp. 1251-1257).
15. Jabalpur.S. Gupte, Paul Infant Teenu Mohandas and J. M. Conrad. (2012). A survey of quadrotor Unmanned Aerial Vehicles. Proceedings of IEEE Southeastcon, (pp. 1-6). Orlando, FL.
16. Samir Bouabdallah, Roland Siegwart. (2005). Backstepping and sliding-mode techniques applied to an indoor micro quadrotor. IEEE International Conference on Robotics and Automation (ICRA). Barcelona, Spain: IEEE International.
17. Shibu Jose, Harminder Pal Singh, Daizy Rani Batish, Ravinder Kumar Kohli. (2013). Invasive Plant Ecology. Boca Raton: CRC Press .
18. Sundarapandian Vaidyanathan, C.-H. L. (Ed.). (2017). Applications of Sliding Mode Control in Science and Engineering. Cham, Switzerland: Springer International Publishing.
19. T. T. Mac, C. Copot, T. T. Duc and R. De Keyser. (2016). AR.Drone UAV control parameters tuning based on particle swarm optimization algorithm. 2016 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR), (pp. 1-6). Cluj-Napoca. doi:10.1109/AQTR.2016.7501380
20. Yang, X.-S. (2008). Nature-Inspired Metaheuristic Algorithms. Frome, UK: Luniver Press.