### Literature Review  
Over time, much research has been performed to develop a robust control strategy for the longitudinal motion of UAVs [11; 12]. The Proportional-Integral-Derivative (PID) control strategy is commonly used in UAVs owing to its simplicity and effectiveness [13]. Although this control method offers computational simplicity and reliability, it struggles with handling unmodeled intrinsic nonlinearities, significant error variations, and random flight disturbances [14]. Moreover, the offline optimization of the PID gains poses an ill-posed problem. The bio-inspired meta-heuristic optimization algorithms, such as particle swarm optimization and ant colony optimization, have been used to tune the PID gains to improve the controller's adaptability under environmental indeterminacies [15; 16]. However, they are inherently prone to premature convergence and high computational complexity [17; 18].  
The advancement of control strategies has led to the exploration of various innovative control techniques for seamless operation [19; 20]. When it comes to handling nonlinear dynamics, the fuzzy logic control scheme exhibits effective responsiveness but it is challenging to design fuzzy rules and tune their membership functions [21]. Despite its reputation for adaptability, the sliding mode control scheme frequently experiences chattering, a symptom brought on by abrupt switching that impairs system performance [22]. The model predictive control scheme also offers robust control actions; however, its real-time implementation is difficult due to the high processing requirements [23]. The H-infinity control reduces fluctuations and disturbances; however, the intended transient response characteristics may not always be achieved because the said controller's constitution requires complex computations [24]. Even though artificial neural network-based controllers can adapt to time-varying systems, their reliance on massive datasets limits their performance, as it is infeasible to collect data regarding every operating condition [25].  
A popular alternative to traditional controllers is the linear quadratic regulator (LQR) which yields optimal control decisions by minimizing a quadratic cost function. It works well in a variety of applications because it can deliver optimal control actions while preserving system stability [26]. However, it lacks robustness against nonlinear disturbances and parametric uncertainties [27]. The LQR is generally integrated with auxiliary control components to adjust to dynamic system requirements. The integration of an integral controller with the typical LQR increases the controller's reference tracking accuracy and robustness against disturbances. However, the dependence of LQR, and its variants, on the system's linearized state space model incapacities them to address modeling uncertainties and identification errors.  
Recently, the learning-based control algorithms have garnered a lot of traction owing to their self-learning and self-tuning ability. They offer a pragmatic solution to the aforementioned constraints because of their enhanced design flexibility which increases their adaptability to manage difficult, time-varying circumstances [28]. They improve performance under unprecedented disturbances by allowing the adaptive control law to learn the system requirements and dynamically modify parameters. Although online learning control algorithms impose a recursive computational burden, modern computing devices possess sufficient resources to efficiently handle this computational overhead.