Quadcopters are researched all over the world. A lot of techniques had been developed and many others a blistering, each one with their pros and cons. The focus of this work is the comparison between the performance of some techniques most used in quadcopters, qualifying these techniques. This work shows the designing process of a quadcopter controllers, starting with state feedback with dominant poles and going to the Linear Quadratic Regulator controller. Focusing on optimizing the performance of those control strategies, computational techniques were used to solve an LQR optimization problem and to help choose the best inputs for the rotors. This work presents a PID controller that will be used as a reference for comparison. The state feedback controller with the Fuzzy position control performed very well, being the fastest one to settle on the Z axis, having the least overshoots and the lowest steadystate errors for the X and Y axes. The Fuzzy controller did what was supposed to do, smoothing and enabling a precise movement for the quadcopter. The GA also did what was supposed to do and improved the settling time for the LQR controller. It showed that it was a nice way to tune the Q and R matrixes, allowing the controller to be the fastest one to settle in the X and Y axes. As a result of this work, the modern control techniques, state feedback and LQR, performance better than the classic PID controller, used as reference.