In the last decades, the quadrotor has been used in many areas, and deigning an effective flight control algorithm for the quadrotor has attracted great interests in both control and robotics communities. This thesis focuses on the flight control of the quadrotor by using different methods: The extend Kalman filter (EKF) based linear quadratic regulator (LQR) method and learning-based model predictive control (LBMPC) method. Chapter 4 investigates the flight control of a quadrotor subject to the model uncertainties and external disturbances. We propose a LQR based tracking algorithm. However, the designed LQR controller is hard to be implemented because of the existing noises in the measured states. A modified EKF is then designed for the online estimation of the position, velocity and motor dynamics by using the measured outputs. From the experimental testing results, it is shown that the proposed EKF-based LQR control method solves the tracking problem of the quadrotor with less tracking errors than only using the LQR method. In Chapter 5, the tracking control problem of the quadrotor subject to external disturbances and physical constraints is studied. A model predictive control (MPC) based algorithm is proposed. To reduce the computational load, a modified prior barrier interior-point method is used to solve the quadratic programming (QP) problem. Nevertheless, the achievable flight performance by using the standard MPC algorithm is affected by external disturbances. A LBMPC algorithm is proposed for the disturbance rejection. From the simulation results, it is shown that using the proposed LBMPC algorithm have less tracking errors than applying the standard MPC algorithm. / Graduate