İHA Yardımcı İniş Sisteminin Meta-Sezgisel Optimizasyon Yöntemleri ile Kontrolü

Year 2021, , 1223 - 1230, 31.10.2021

Serkan Çaşka , Ali Uysal

https://doi.org/10.35414/akufemubid.888652

Cited By: 3

Abstract

Dikey iniş/kalkış yapabilen tip insansız hava aracı(İHA) bir zemine inerken, İHA’nın GPS hassasiyetinin düşük olması ve şasisindeki titreşim nedeniyle hedeflenen iniş noktası ile gerçek iniş noktası arasında yatay düzlemde bir miktar hata oluşur. Bu çalışmada, İHA'nın yatay düzlemde iniş yapması sırasında meydana gelen konumlandırma hatasına göre hareket eden bir sistemin kontrolü yapılmıştır. İHA'nın hedef iniş noktasına göre konumunu algılamak için iki dijital kamera içeren bir stereo kamera sistemi kullanılmıştır. İHA'nın gerçek zamanlı konumu, Visual Studio'ya entegre edilmiş olan OpenCV kitaplığı kullanılarak hesaplanmıştır. Elde edilen İHA konumu, yatay düzlemde hareket edebilen bir platformun doğru akım motorları için hedef konum olarak kullanılmıştır. Sistem üzerinde yapılan denemeler kapalı mekanda ve gerçek çalışma koşullarında sağlanmıştır. İniş sistemine entegre edilmiş iki motoru kontrol eden orantı, integral ve türev(PID) tipi denetleyici katsayılarını bulmak için Genetik Algoritma(GA) ve Parçacık Sürüsü Optimizasyonu(PSO) yöntemleri kullanılmıştır. Geliştirilen denetleyicilerin performans sonuçları tablo halinde sunulmuştur.

Keywords

İnsansız Hava Aracı, Parçacık sürüsü optimizasyonu, Genetik algoritma, PID kontrolör

Control of UAV Auxiliary Landing System with Meta-Heuristic Optimization Methods

Abstract

While an unmanned aerial vehicle(UAV) with vertical take off and landing capability is landing onto a ground, a horizontal positioning error occurs between actual landing point and target landing point. This error occurs because of vibration on UAV chasis during flight and low GPS accuracy. In this study, control of an automatic assistive landing system that moves according to the horizontal positioning error that UAV made during its landing was carried out. A stereo camera system with two digital cameras were placed onto a moving platform that UAV lands. Cameras were used to detect actual position of the landing UAV. Real time position of landing UAV was computed by using OpenCV library added into Visual Studio. The calculated position of the landing UAV was defined as target position for two DC motors of moving platform that has horizontal motion capability. The tests of this system were performed in real and indoor conditions. Genetic algorithm (GA) and Particle Swarm Optimization(PSO) algorithm were used to calculate the coefficients of controllers that were defined as Proportional-Integral-Derivative(PID) controllers. Developed controllers control two DC motors of the moving plate of the system. Success of the controllers were compared in table form.

Keywords

Unmanned Aerial Vehicle, Particle swarm optimization, Genetic algorithm, PID controller

References

• Abo-Hammour, Z. S., Asasfeh, A. G., Al-Smadi, A. M. and Alsmadi, O. M., 2011. A novel continuous genetic algorithm for the solution of optimal control problems, Optimal Control Applications&Methods, 32(4), 414–432
• Çınar S. M., Balcı Z. and Yabanova İ., 2019. Performing Speed Control of a DC Motor with Auto-Tuning PID, Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 19, 690-696
• Choi, C.H., Jang, H.J., Lim, S.G., Lim, H.C., Cho, S.H., Gaponov, I., 2016. Automatic wireless drone charging station creating essential environment for continuous drone operation. In Proceedings of the 2016 International Conference on Control, Automation and Information Sciences, ICCAIS 2016, Ansan, South Korea, 27–29 October,
• Feng, Y., Zhang, C., Baek, S., Rawashdeh, S. And Mohammadi, A., 2018. Autonomous Landing of a UAV on a Moving Platform Using Model Predictive Control, Drones, 2(4), 34.
• Jayachitra, A. and Vinodha, R., 2014. Genetic Algorithm Based PID Controller Tuning Approach for Continuous Stirred Tank Reactor, Advances in Artificial Intelligence, 2014,1-8.
• Junaid, A.B., Konoiko, A., Zweiri, Y., Sahinkaya, M.N., and Seneviratne, L. 2017. Autonomous wireless self-charging for multi-rotor unmanned aerial vehicles, Energies, 10(6), 803.
• Kealy,T. and O’Dwyer, A., 2003. Analytical ISE Calculation And Optimum Control System Design. Proceedings of the Irish Signals and Systems Conference, Limerick, Ireland, July, 418-423.
• Maier, M., Oeschger, A. and Kondak, K., 2015. Robot-assisted Landing of VTOL UAVs: Design and Comparison of Coupled and Decoupling Linear State-space Control Approaches. IEEE Robotics and Automation Letters, 1(1), 114-121.
• Modares, H. and Naghibi-Sistani, M.-B., 2011. Solving nonlinear optimal control problems using a hybrid IPSO-SQP algorithm, Engineering Applications of Artificial Intelligence, 24(3), 476–484
• Mukhtar, A., Tayal, V. K. and Singh, H., 2019. PSO Optimized PID Controller Design for the Process Liquid Level Control, 3rd International Conference on Recent Developments in Control, Automation & Power Engineering (RDCAPE), Noida, India, 10-11 October, 590-593.
• Nezhadhosein, S., Heydari, A. and Ghanbari R., 2015. A Modified Hybrid Genetic Algorithm for Solving Nonlinear Optimal Control Problems, Mathematical Problems in Engineering, 2015, 1-21
• Rao, P. V. G. K., Subramanyam, M. V. and Satyaprasad, K., 2014. Study on PID Controller Design and Performance Based on Tuning Techniques, International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), Kanyakumari, India, 10-11 July, 1411-1417
• Wills, A., Mills, A. and Ninness, B., 2009. A MATLAB Software Environment for System Identification?. Proceedings of the 15th IFAC Symposium on System Identification, Saint-Malo, France, 6-8 July, 741-746.