Attitude controller optimization of four

Quadrocopters, which are getting much popular day to day, are unmanned air vehicles which have four rotors to fly and maneuver in the air. They are used in military or commercial areas. Researchers are also interested in quadrocopters because their physical structure is simple; they can be modeled linear or nonlinear; and many problems about control, optimization, or artificial intelligence can be studied on it. A stable quadrocopter design requires firstly a good controller design. There are many control methods applied to quadrocopters but the most used one is the PID controller. Every controller needs to be tuned well for a good performance. Optimization algorithms are one of the popular tools for tuning the controllers. They can be used to produce optimum controller parameters with less experiment and in a short time. Each optimization algorithm has a different characteristic and different performance depending on the problem. So a comparison is needed among some popularly used optimization algorithms. In this study, a quadrocopter is modeled, and four PID controllers are designed to control attitude and hover. The optimum parameters for controllers are determined by the artificial bee colony algorithm, particle swarm optimization, and genetic algorithm. Performance of the controllers and optimization algorithms is given comparatively