Şahin YILDIRIM, Nihat ÇABUK, Veli BAKIRCIOĞLU

Dodecarotor İHA'nın Yörünge Takibi için Bozkurt Optimizasyon Algoritması Temelli Optimal PID Denetleyici Tasarımı

Bu çalışmada, bir dodekarotor İHA'nın minimum yörünge hatası ile yönelimini ve konumunu kontrol etmek için optimal PD kontrolör kazançlarını bulmayı amaçladık. Bu bağlamda, İHA'nın uçuş kontrolünde her bir durum (yuvarlanma, yunuslama, yalpalama, yükseklik) için iç döngüde hız geri beslemesi ve dış döngüde konum geri beslemesi olan kademeli bir PD kontrolör yaklaşımı benimsenmiştir. Sonrasında, yörünge izleme performansı açısından çok etkin olmayan sapma açısı hariç, her durum için sistemin zaman alanı yanıtına ve yörünge izleme hatasına dayalı bir uygunluk fonksiyonu tanımlandı. Tanımlanan uygunluk fonksiyonunu en aza indirerek PD kazanımlarını elde etmek için Bozkurt Optimizasyon Algoritması (GWO) kullanıldı. Aynı zamanda, GWO'dan elde edilen sonuçları kıyaslamak ve sığ bir çözüm alanından kaçınmak için Parçacık Sürü Optimizasyonu (PSO) kullanıldı. Her iki algoritmanın optimizasyon çalışması sonucunda elde edilen PD kontrolör parametreleri sisteme uygulanmış ve sonuçlar birbirleriyle karşılaştırılmıştır. Nihai olarak, her iki algoritma için en iyi sonuçları sağlayan kazançlar birbirleri ile karşılaştırılmış ve sonuçlar zaman alanı sonuçları ve aktüatör giriş düzgünlüğü açısından tartışılmıştır.

Anahtar Kelimeler:

PID Kontrol, GWO, Optimizasyon, İHA

OPTIMAL PID CONTROLLER DESIGN FOR TRAJECTORY TRACKING OF A DODECAROTOR UAV BASED ON GREY WOLF OPTIMIZER

In this study, we aimed to find optimal PD controller gains to control orientation and position of a Dodecarotor UAV with minimum trajectory error. In this context, a cascaded PD controller approach which has velocity feedback in the inner loop and position feedback in the outer loop was adopted for each state (roll, pitch, yaw, altitude) in the flight control of the UAV. Subsequently, a fitness function was defined based on the system's time domain response and trajectory tracking error for each state, except the yaw angle, which is non-dominant in terms of trajectory tracking performance. Grey Wolf Optimizer (GWO) was used to obtain PD gains by minimizing the defined fitness function. At the same time, Particle Swarm Optimizer was used in order to benchmark the obtained results from GWO and to avoid a shallow solution space. The obtained PD controller parameters as a result of the optimization study of both algorithms were implemented to the system and the results were compared with each other. Finally, the gains that provided the best results for both algorithms were compared with each other and the results were discussed in terms of the time domain results and the actuator input smoothness. It has been observed that the GWO optimized controller provides a 40-46% improvement over PSO in all four different mass UAVs in terms of reducing axis position errors.

Keywords:

PID control, GWO, Optimization, UAV,

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