The goal of this thesis is to propose reliable control laws for the motion planning of a multicopter system under constraints and unexpected events (e.g., actuator faults). A hierarchical control architecture which decouples the scheme into position and attitude control is proposed. At the high level the position controller calculates the position error and provides the desired thrust and angles to the attitude controller at the low level to stabilize the system around the desired angles. The scheme's reliability (i.e., ensuring feasibility, stability and constraint validation) is done through a coherent merging of differential flatness, feedback linearization and Nonlinear Model Predictive Control (NMPC). Hence, the main thesis contributions lie in:i) The analysis and design of bounds which characterize the various inputs and states of the system (angle position and velocity, torques, etc.). These are subsequently applied for constrained trajectory design (which combines differential flatness and feedback linearization through the use of B-spline parametrizations).ii) Designs which exploit the ``computed-torque control law'' as local control within an NMPC with recursive feasibility guarantees. We show that avoiding the standard linearizations employed for nonlinear dynamics improves performance (in the sense of reducing the prediction horizon, enlarging the terminal region and reducing the problem's complexity). Further advances relax the requirement of set invariance and even discard the need for terminal stabilizing constraints. Generalizations for similar feedback linearizable systems are discussed.iii) A hierarchical optimization-based FTC (Fault Tolerant Control) scheme to counteract a stuck rotor fault. This is done through control reconfiguration at both high and low levels, coupled with a fault diagnosis mechanism capable of handling fault detection, isolation and estimation.The results are validated over extensive simulations and laboratory experiments involving a nano-quadcopter.