Abstract




 
   

IJE TRANSACTIONS B: Applications Vol. 32, No. 5 (May 2019) 747-758   

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  DESIGN OF AN INTELLIGENT CONTROLLER FOR STATION KEEPING, ATTITUDE CONTROL, AND PATH TRACKING OF A QUADROTOR USING RECURSIVE NEURAL NETWORKS
 
E. Khosravian and H. Maghsoudi
 
( Received: February 08, 2019 – Accepted in Revised Form: May 02, 2019 )
 
 

Abstract    The interest in unmanned aerial vehicles (UAVs) has grown in recent years. Moreover, the necessity to control and navigate these vehicles has attracted much attention from researchers in this field. This is mostly due to the fact that the interactions between turbulent airflows apply complex aerodynamic forces to the system. Since the dynamics of a quadrotor is non-linear and the system is a multivariable one, moreover, it has six degrees of freedom for only four control inputs, then it is an underactuated system. This is why conventional control algorithms employed to track desired trajectories of fully actuated aerial vehicles are no longer applicable for quadrotors. The design of a controller which stabilize the aerial vehicle in the presence of uncertainties and disturbances, then navigate it along a desired trajectory, is the main step in the manufacturing of a fully autonomous unmanned aerial vehicles. The aim of this study is to design and implement an intelligent controller for station keeping, altitude control, and path tracking of a quadrotor. For this purpose, an artificial neural network method was employed. The artificial neural network is one of the most powerful and useful tools in the modification of a control system. In this study, the control methods applied to quadrotors is reviewed at first. Then in order to analyze the behavior of the system and also to design the controller, the state equations of a quadrotor are discussed. Afterwards, the design of recurrent neural networks based non-linear PID control algorithm is presented. Finally, the results of the simulation performed are presented and the performance of the proposed algorithm are investigated. It was shown that the proposed algorithm successfully makes the quadrotor tracks the desired trajectory and also stabilizes its attitude.

 

Keywords    Artificial Neural Network; Intelligent Controlling; Non-Linear PID Control; Quadrotor; Station Keeping; Trajectory Tracking

 

چکیده   

در سال¬های اخیر تمایل فزاینده¬ای برای بهره¬برداری از وسایل پرنده بدون سرنشین و پژوهش پیرامون آن-ها پدید آمده است. در همین راستا، حفظ موقعیت، حفظ وضعیت و تعقیب مسیر این دسته از پرنده¬ها نیز اهمیت فراوانی پیدا کرده است. دلیل این مسئله آن است که برهم¬کنش میان جریان¬های اغتشاشی نیروهای آیرودینامیکی پیچیده¬ای را به سامانه اعمال می¬کنند. به جهت آن که دینامیک یک کوادروتور غیرخطی بوده و سامانه از نوع چندمتغیره می¬باشد، مضاف بر آن سامانه دارای شش درجه¬ی آزادی برای تنها چهار ورودی کنترلی است، بنابراین می¬توان آن را یک سامانه¬ی زیرتحریک در نظر گرفت. به همین دلیل است که در مورد کوادروتورها نمی¬توان از الگوریتم¬های متداول مورد استفاده برای تعقیب مسیر پرنده¬های بدون سرنشین دیگر بهره برد. گام اساسی در ساخت یک پرنده¬ی بدون سرنشین کاملاً خودکار، طراحی یک کنترلر برای پایدارسازی آن در حضور اغتشاشات محیطی و عدم قطعیت¬ها و نیز راهبری در یک مسیر مشخص است. هدف این پژوهش، طراحی و اجرای یک کنترلر هوشمند برای حفظ موقعیت، کنترل وضعیت و تعقیب مسیر یک کوادروتور است. در این پژوهش، برای نیل به این هدف از روش شبکه¬ی هوش مصنوعی استفاده شد. این روش یکی از ابزارهای بسیار کارآ و مفید در بهینه¬سازی یک سامانه¬ی کنترلی است. در این مقاله، ابتدا روش¬های کنترلی متداول که بر کوادروتورها اعمال می¬شود، مرور شده است. در ادامه، به¬منظور تحلیل رفتار سامانه و نیز طراحی کنترلر، معادلات حالت یک کوادروتور ارائه و مورد بحث قرار گرفته است. سپس، نحوه¬ی طراحی الگوریتم کنترلی PID غیرخطی مبتنی بر شبکه¬ی هوش مصنوعی بازگشتی ارائه می¬شود. درنهایت، نتایج شبیه¬سازی¬های صورت¬گرفته ارائه و بر اساس این نتایج، عملکرد الگوریتم پیشنهادی بررسی می¬شود. نتایج شبیه¬سازی نشان می¬دهد که با استفاده از الگوریتم پیشنهادی، هر دو هدف حفظ وضعیت و تعقیب مسیر مطلوب توسط کوادروتور تامین می¬شود.

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