Our research objective consists in developing a, model-based, automatic safety recovery system, for a small-scale helicopter Unmanned Aerial Vehicle (UAV) in autorotation, i.e. an engine OFF flight condition, that safely flies and lands the helicopter to a pre-specified ground location. In pursuit of this objective, the contributions of this thesis are structured around three major technical avenues. The first one concerns the modeling of the nonlinear dynamics of a small-scale helicopter UAV. We have developed a nonlinear, first-principles based, high-order model, used as a realistic small-scale helicopter simulation environment. This helicopter model is applicable for high bandwidth control specifications, and is valid for a range of flight conditions, including (steep) descent flight and autorotation. This comprehensive model is used as-is for controller validation, whereas for controller design, approximations of this nonlinear model are considered. The second technical avenue addresses the development of a guidance module, or Trajectory Planner (TP), which aims at generating feasible and optimal open-loop autorotative trajectory references, for the helicopter to follow. We investigate two such TP methods. The first one is anchored within the realm of nonlinear optimal control, and allows for an off-line computation of optimal trajectories, given a cost objective, nonlinear system dynamics, and controls and states equality and inequality constraints. The second approach, based upon the concept of differential flatness, aims at retaining a high computational efficiency, e.g. for on-line use in a hard real-time environment. The third technical avenue considers the Trajectory Tracker (TT), which compares current helicopter state values with the reference values produced by the TP, and formulates the control inputs to ensure that the helicopter flies along these optimal trajectories. Since the helicopter dynamics is nonlinear, the design of the TT necessitates an approach that tries to respect the system’s nonlinear structure. Here we have selected the robust control ? paradigm. In particular, our simulations show that the crucial control of helicopter vertical position and velocity exhibit outstanding behavior, in terms of tracking performance. However, the tracking of horizontal position and velocity could potentially be improved by considering some other control methods, such as Linear Parameter-Varying (LPV) ones. To this end, we present an approach that approximates a known complex nonlinear model by an affine LPV model. The practicality of this LPV modeling method is further validated on a point-mass pendulum example, and in the future this method could prove useful when applied to our helicopter application.