### Main Contributions  
This study mainly contributes to formulating a novel online Reconfigurable Learning Control Algorithm (RLCA) for robust-optimal longitudinal motion control of UAVs, enhancing its trajectory tracking accuracy and disturbance compensation. The short-period model is employed to model the longitudinal motion dynamics of a UAV. The fixed-gain Linear Quadratic Integral (LQI) controller is used as the baseline controller. The proposed RLCA is formulated by integrating a dissipative term, an anti-dissipative term, and a model-reference tracking term in the derivative-based learning law. Additionally, the RLCA is augmented with an auxiliary adaptation block that serves as a superior regulator to dynamically adjust the contribution of each control term in generating the final control action. The proposed control framework is designed to enhance the closed-loop system's resilience by leveraging adaptive learning strategies while ensuring optimal performance across different flight regimes. The novel contributions of this article are listed as follows:  
1. Constitution of a baseline LQI control law for the longitudinal motion control of a UAV. The asymptotic stability of the baseline LQI tracking controller is analyzed in a subsequent discussion.
2. Formulation of the proposed RLCA framework that synergistically combines the dissipative, anti-dissipative term, and model-reference tracking control action in the learning control law.
3. Augmentation of the RLCA with a superior layer of state-error-driven hyperbolic scaling functions to autonomously modify the learning gains, ensuring robustness against disturbances.
4. Validation of the enhanced performance of the adaptive RLCA scheme over the baseline LQI controller via customized MATLAB simulations.  
The proposed adaptive RLCA framework significantly enhances the system's ability to handle disturbances while maintaining efficient and stable operation. The proposed framework offers several advantages over conventional adaptive controllers. The anti-dissipative control term in the RLCA improves disturbance by ensuring rapid compensation of large perturbations. The dissipative term minimizes the control energy expenditure, preventing unnecessary actuator strain. Finally, the model-reference tracking term yields optimal tracking decisions while preserving closed-loop stability. The adaptive scaling of the learning gains ensures smooth transitions between control phases, avoiding sudden control discontinuities and mitigating chattering effects.  
The development and validation of the proposed adaptive RLCA framework for robust longitudinal motion control of a UAV system have not been explored in the existing scientific literature. This study addresses this gap by presenting an innovative approach.  
The remainder of the paper is structured as follows: Section 2 outlines the system's mathematical model and the constitution of the baseline LQI tracking controller. Section 3 details the formulation of the proposed RLCA framework. Section 4 discusses the offline parameter tuning procedure. Section 5 presents a comparative analysis of the proposed control scheme based on customized MATLAB simulations. Finally, Section 6 concludes the paper.