Over the past decade, unmanned aerial vehicles (UAVs) have received a significant attention due to their diverse capabilities for non-combatant and military applications. The primary aim of this study is to unveil a clear categorization overview for more than a decade worth of substantial progress in UAVs. The paper will begin with a general overview of the advancements, followed by an up-to-date explanation of the different mechanical structures and technical elements that have been included. The paper will then explore and examine various vertical take-off and landing (VTOL) configurations, followed by expressing the dynamics, applicable simulation tools and control strategies for a Quadrotor. In conclusion to this review, the dynamic system presented will always face limitations such as internal and/or external disturbances. Hence, this can be minimised by the choice of introducing appropriate control techniques or mechanical enhancements.

The power to move aircraft control surfaces has advanced from being manually generated (by the pilot and transmitted via rods and links) to electrically transmitted (via wires) to operate control surface actuators. Various hydraulic, electromagnetic, and electromechanical architectures have been developed to provide the necessary power and to maintain the expected redundancy. Numerous aircraft actuator system designs have been proposed in the past decades, but a comprehensive review has yet to be undertaken. This review paper aims to fill this gap by providing a critical review of the actuation system designs developed for a variety of aircraft. The review focuses on aircraft actuator system designs, namely: electrohydraulic actuator systems, electromechanical actuator systems, and the force-fighting effect in redundant actuation systems. The significance and operational principle of each actuator system are critically analysed and discussed in the review. The paper also evaluates the solution proposed to address force-fight equalization (or force-fight cancelation) in force or torqued-summed architectures. Future trends in redundant actuation system development with reduced force-fighting effect in aircraft actuator systems are also addressed.

This study introduces GAT-CAMDA, a novel framework for the structural health monitoring (SHM) of composite materials under temperature-induced variability, leveraging the powerful feature extraction capabilities of Graph Attention Networks (GATs) and advanced domain adaptation (DA) techniques. By combining Maximum Mean Discrepancy (MMD) and Correlation Alignment (CORAL) losses with a domain-discriminative adversarial model, the framework achieves scalable alignment of feature distributions across temperature domains, ensuring robust damage detection. A simple yet at the same time efficient data augmentation process extrapolates damage behaviour across unmeasured temperature conditions, addressing the scarcity of damaged-state observations. Hyperparameter optimisation via Optuna not only identifies the optimal settings to enhance model performance, achieving a classification accuracy of 95.83% on a benchmark dataset, but also illustrates hyperparameter significance for explainability. Additionally, the GAT architecture’s attention demonstrates the importance of various sensors, enhancing transparency and reliability in damage detection. The dual use of Optuna serves to refine model accuracy and elucidate parameter impacts, while GAT-CAMDA represents a significant advancement in SHM, enabling precise, interpretable, and scalable diagnostics across complex operational environments.

This study introduces GAT-CAMDA, a novel framework for structural health monitoring (SHM) of composite materials under temperature-induced variability, leveraging the powerful feature extraction capabilities of Graph Attention Networks (GATs) and advanced domain adaptation techniques. By combining Maximum Mean Discrepancy (MMD) and Correlation Alignment (CORAL) losses with domain-discriminative adversarial layer, the framework achieves precise alignment of feature distributions across temperature domains, ensuring robust damage detection without relying on labelled target data. A bespoke data augmentation process extrapolates damage behaviour across unmeasured temperature conditions, addressing the scarcity of damaged-state observations. Hyperparameter optimisation via Optuna not only identifies optimal settings to enhance model performance, achieving a classification accuracy of 95.83% on a benchmark dataset but also illustrates hyperparameter significance for explainability. Additionally, the GAT architecture’s attention demonstrates the importance of various sensors, enhancing transparency and reliability in damage detection. The dual use of Optuna serves to refine model accuracy and elucidate parameter impacts, while GAT-CAMDA represents a significant advancement in SHM, enabling precise, interpretable, and scalable diagnostics across complex operational environments.

Renewable energy integration into microgrids has become a key approach to addressing global energy issues such as climate change and resource scarcity. However, the variability of renewable sources and the rising occurrence of High Impact Low Probability (HILP) events require innovative strategies for reliable and resilient energy management. This study introduces a practical approach to managing microgrid resilience through Explainable Deep Reinforcement Learning (XDRL). It combines the Proximal Policy Optimization (PPO) algorithm for decision-making with the Local Interpretable Model-agnostic Explanations (LIME) method to improve the transparency of the actor network's decisions. A case study in Ongole, India, examines a microgrid with wind, solar, and battery components to validate the proposed approach. The microgrid is simulated under extreme weather conditions during the Layla cyclone. LIME is used to analyse scenarios, showing the impact of key factors such as renewable generation, state of charge, and load prioritization on decision-making. The results demonstrate a Resilience Index (RI) of 0.9736 and an estimated battery lifespan of 15.11 years. LIME analysis reveals the rationale behind the agent's actions in idle, charging, and discharging modes, with renewable generation identified as the most influential feature. This study shows the effectiveness of integrating advanced DRL algorithms with interpretable AI techniques to achieve reliable and transparent energy management in microgrids.

This study addresses the challenges of energy deficiencies and high impact low probability (HILP) events in modern electrical grids by developing resilient microgrid energy management strategies. It introduces a sliding Model Predictive Control (MPC) methodology integrated with Battery Energy Storage Systems (BESS), emphasizing extending battery life and prioritizing critical loads during HILP events. This approach focuses on extending the sustainability of battery operation by linearizing the battery lifecycle within the optimization framework. Furthermore, this research proposed a straightforward method to mitigate communication disruptions during HILP events, thereby ensuring operational integrity. This focused approach enhances isolated microgrid resilience and sustainability, offering a strategic response to contemporary environmental challenges.