The increasing complexity of missions that UAVs must perform has resulted in the development of more complex control techniques. Over the years linear and nonlinear Proportional-Derivative (PD) controllers have been widely used to control the attitude and position of an UAV. Nevertheless, when the system is constantly disturbed or there are uncertainties in the model, controllers are not capable to achieve the desired position or their performance is degraded considerably. In this part of the book we focus on the development of different control techniques to improve the performance of the closed-loop system and the navigation mission.

The first three chapters cover the development of the control schemes. Five approaches are addressed in these chapters: two are based on the saturation function (Chaps. 6 and 8), one is based on the sliding modes (Chap. 7), one uses backstepping (Chap. 6), and the last includes a feedback in combination with the Uncertainty Disturbance Estimator (Chap. 8).

Finally, the last three chapters focus on how to improve a mission. For example, on the one hand, some inspection and surveillance tasks require that the aerial vehicle passes through specific points with constraints on the velocity or time. Thus the trajectory generation and path tracking problem needs to be solved here. In Chap. 9 this problem is addressed and a solution is proposed. On the other hand, autonomous navigation in an unstructured environment also needs algorithms for obstacle avoidance. In Chap. 10 a control scheme is proposed to avoid fixed obstacles using the Artificial Potential Field (APF) and the limit cycle approaches. Notions for beginners in this field are introduced here. Finally, in Chap. 11 a tool to improve manual flight when the vehicle is out of reach is discussed. A haptic teleoperation algorithm is proposed for collision-free navigation. In this scheme a solution is also presented for pose estimation in indoor environments using visual information.