In the last decade, the quadrotor has been adopted in many application areas. 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 two different methods:​ The extend Kalman filter (EKF)-based linear quadratic regulator (LQR) method and the learning-based model predictive control (LBMPC) method.

This thesis investigates the flight control of a quadrotor subject to model uncertainties and external disturbances. An LQR-based tracking algorithm is proposed. 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. The simulations and experimental results are provided to evaluate the proposed algorithm.

The tracking control problem of the quadrotor subject to external disturbances and physical constraints is also studied. A model predictive control (MPC)-based tracking control algorithm is adopted. To reduce the computational load, a modified prior barrier interior-point method is adopted to solve the quadratic programming (QP) problem. Nevertheless, the achievable flight performance by using the standard MPC algorithm is affected by external disturbances. An LBMPC algorithm is introduced for the disturbance rejection. The simulation results obtained from the LBMPC algorithm are also provided.