## 1 Introduction  
An unmanned aerial vehicle (UAV) is a space-traversing vehicle capable of flying without a human crew on board and can be remotely controlled or operate autonomously [1]. During the last few decades, UAVs have gained significant attention in civilian domains, surpassing their traditional association with military and defense applications. Particularly, such systems are now extensively exploited in various sectors, including precision agriculture [2; 3; 4], construction industry [5], air taxis, and merchandise delivery [6]. In the vein of those applications, a commonly arising problem is the design of an effective feedback controller such that the closed-loop system tracks a desired reference altitude and airspeed velocity. For example, consider a scenario where a user intends to manually command a UAV to fly over a forest area for the purpose of detecting potential wildfires. Manual control of the UAV presents difficulties as it requires applying complex control inputs, such as roll, pitch, or/and yaw commands, to maintain the vehicle's desired cruising speed along a predefined trajectory, even without the presence of external disturbances. Thus, the exploitation of automatic control techniques becomes indispensable in order to overcome the limitation of manual operation and ensure precise and safe maneuvering of the UAV. One of the most commonly used controllers in autopilots, is the PID because of its implementation simplicity and low computational burden [7; 8]. Moreover, approximation-based techniques utilized in autopilots include fuzzy logic [9] and neural network algorithms [10] that estimate the model of the UAV, at the expense of computational complexity of the control scheme. An alternative approach is to approximate their dynamics using linear models. One effective method to enhance altitude control performance is to exploit Linear Quadratic Gaussian controllers [11]. Furthermore, \(\mathcal{H}_{\infty}\) loop-shaping methods can be applied to small fixed-wing UAVs, especially in situations involving noise or varying payload conditions, to achieve improved control performance [12]. A novel system-decomposition-based robust control method for linearized fixed-wing UAVs is introduced in [13]. By decomposing the system into simpler subsystems, the controller design becomes easier and the overall complexity is reduced. In particular, each subsystem utilizes an extended state-observer-based robust control method to enhance tracking performance and handle disturbances. Alternatively, the authors in [14] proposed a feedback linearization control scheme dealing with the nonlinear equations of motion. Nevertheless, a precise model of the system is required. In aircraft flight control, the aerodynamic forces and moments acting on the aircraft are nonlinear functions that cannot be modeled exactly and thus achieving perfect cancellation of such nonlinearities becomes unattainable. Consequently, there are inherent limitations associated with employing exact state feedback linearization, particularly when confronted with unmodeled dynamics and uncertainties in system parameters. Furthermore, Model Predictive Control (MPC) is commonly employed to address the trajectory tracking problem [15; 16; 17]. Although, MPC is a method with great applications, mainly in slowly evolving systems, the computational burden associated with their iterative nature should be taken into consideration during their practical implementation in fast dynamical systems such as UAVs. Backstepping is another well-studied nonlinear technique in the related literature [18; 19; 20]. An adaptive backstepping flight controller for a high-performance UAV was designed in [21], addressing various constraints such as saturations, bandwidth limitations, and rate limits which are encountered in all practical control systems. To facilitate the control design, a linear aerodynamic model was employed in addition to adaptation laws integrated into the system in order to enable online estimation of the stability derivatives within the model.