https://jast.ias.ir/ Journal of Aerospace Science and Technology en daily 1 Wed, 01 Apr 2026 00:00:00 +0330 Wed, 01 Apr 2026 00:00:00 +0330 https://jast.ias.ir/article_235170.html The objective of this paper is to design an altitude controller for an aircraft using fuzzy logic and genetic algorithms. In this study, two methods (specialist experience and scale mapping) are used to develop the fuzzy rules. First, a basic fuzzy controller is designed using both methods. Then the centers of the membership functions of inputs and outputs are determined using genetic algorithm, aiming to reduce control effort, steady-state error, and rise time. The TOPSIS method is employed to select the final solution. The results show that, on average, the optimized fuzzy controllers achieve performance improvements of 28%, 53%, and 15% in control effort, steady-state error, and rise time, respectively. Finally, robustness analysis is conducted for the optimized fuzzy controllers in the presence of uncertainties: sensor noise, different aerodynamics derivatives, changing flight conditions, and gust. The results indicate that the optimized fuzzy controller based on specialist experience demonstrates robust performance. https://jast.ias.ir/article_235173.html Achieving agreement among agents is crucial in multi-agent systems to enable coordinated operations and synchronized path alignment. These agreements enhance safety, streamline planning, reduce interference, and improve efficiency. Coordination methods vary based on factors such as prioritization, routes, constraints, and shared information, with agreements established directly, interactively, or via a central agent. This study explores the coordinated control of multiple aircraft for synchronized landings using a fuzzy control allocation approach. A fuzzy controller is implemented and its parameters optimized through the NSGA-II genetic algorithm, ensuring effective distribution of control signals between elevator operators and thrust vector systems. This approach enables synchronized, automated landings from different initial altitudes, maintaining stability with minimal control effort across two scenarios. In the first, agents coordinate landings without ground obstacles, where one agent leads and others follow. The second scenario incorporates a ground obstacle into the coordination process. In both cases, the approach ensures synchronized landings with minimal control effort, high precision, and stable performance. Simulation results demonstrate that the optimized fuzzy controller successfully achieves coordinated automatic landings in both scenarios, handling obstacles effectively. The system ensures synchronized landings with reduced control effort, high accuracy, minimal time, and reliable stability. https://jast.ias.ir/article_224359.html To enhance engine efficiency, extend the lifespan of components, and achieve more complete combustion, controlling the exhaust gases from the combustion chamber plays a crucial role in gas turbine design. This control can also be applied to cooling systems, which optimize the utilization of cooling air and improve the flow pattern within the combustion chamber. Notable progress has been made in utilizing these systems. One of the latest models of combustion chambers is the T56 series 3.5. While the general features of this chamber are identical to those of Series 3 and Series 2, the difference lies in the number and location of wall holes. Unfortunately, none of the manufacturer's technical specifications have been published. This paper aims to compare and assess these chambers' geometric and technical specifications through numerical modeling, using the base model (T56 series 3) as a reference. This localization of design knowledge for these chambers has been achieved. Simulation results indicate that flow rotation is almost eliminated in the secondary and dilution regions of the new chamber, and the thickness of the wall cooling layer remains nearly constant throughout the combustion chamber. https://jast.ias.ir/article_224360.html The proper selection of components and precise optimization of operating parameters in multi-rotor systems to achieve mission objectives with maximum efficiency has long been a critical concern for drone manufacturers. This study introduces a comprehensive and adaptive model for the calculation, analysis, and optimization of these parameters by thoroughly investigating key variables and integrating them into a unified framework. Initially, the fundamental concepts of multi-rotor systems and their core subsystems were examined. Subsequently, the dynamic relationship between these elements was established by analyzing drone performance in both full-throttle and hover-throttle conditions. A MATLAB-based algorithm was developed and validated using experimental data from component manufacturers, which serve as a primary reference for multi-rotor system evaluations under diverse flight scenarios. This algorithm enables analysis of all performance-influencing parameters, helping to resolve design uncertainties before manufacturing or actual flight. Furthermore, it facilitates the identification of the optimal component configuration based on predefined mission requirements and environmental conditions. https://jast.ias.ir/article_230229.html Phase change materials (PCMs) have a high capacity for storing and releasing thermal energy at various temperatures, making them particularly significant in thermal and combustion systems. The main goal of this research is to investigate the effect of adding PCMs to natural gas fuel on flame characteristics and combustion performance. The selected PCMs include water, ammonia, ethane, propane, and argon. The chemical mechanism used in this research is GRI 3.0, and the simulations were conducted using CHEMKIN software. The results indicate that the addition of PCMs to the fuel reduces both the total lift-off length and flame length. Furthermore, incorporating these materials decreases flame temperature and the emissions of key pollutants, including nitrogen oxides (NOx), carbon monoxide (CO), and carbon dioxide (CO₂). The simulation results of this research show good agreement with both experimental and numerical findings from previous studies. The simulation results of this research show good agreement with both experimental and numerical findings from previous studies. https://jast.ias.ir/article_230230.html The optimization of membership function parameters in Fuzzy-PID controllers presents significant computational and design challenges, often relying on time-consuming and heuristic-based methods that lack generalizability. In this paper, we present a novel and efficient technique to enhance the performance of Fuzzy Inference Systems (FIS) by employing Deep Reinforcement Learning (DRL) for automatic and intelligent parameter tuning. Fuzzy logic offers significant advantages in handling nonlinear, uncertain, and highly dynamic systems due to its rule-based reasoning and flexibility. Meanwhile, DRL has demonstrated outstanding capabilities in learning optimal control strategies in complex environments through trial-and-error interactions and policy optimization. By combining the strengths of both paradigms, our proposed method enables the automatic adjustment of membership function parameters without manual tuning, leading to improved control accuracy and system adaptability. The proposed DRL-Fuzzy PID framework is specifically applied to the control of Unmanned Aerial Vehicles (UAVs), which are highly nonlinear systems that demand robust, adaptive, and precise trajectory tracking. Additionally, by strategically reducing the number of parameters involved in the optimization process, we significantly shorten the learning time and reduce computational overhead. Extensive UAV simulation studies confirm the robustness and efficiency of the proposed approach, showing substantial improvements in tracking performance and overall flight stability compared to traditional fuzzy controllers. This hybrid DRL-Fuzzy strategy offers a promising solution for advanced UAV control applications where adaptability and precision are critical. https://jast.ias.ir/article_230231.html High-precision terminal guidance for unmanned combat aerial vehicles (UCAVs) requires integrated guidance and control (IGC) strategies capable of managing nonlinear dynamics, subsystem interactions, and time-varying disturbances. Conventional design approaches treat guidance and control separately, often resulting in reduced accuracy and degraded stability in operational scenarios. This study addresses this limitation by investigating whether a hybrid adaptive controller—combining an offline-trained deep neural network (DNN) with a fuzzy inference system—can significantly improve interception performance in loitering munition missions. The hypothesis is that integrating DNN-based gain scheduling with fuzzy adaptation will reduce interception time and engagement range while maintaining robust stability under uncertainties. The IGC model is fully derived for a 3‑D engagement scenario, and three controllers are implemented: a classical PID, an adaptive offline DNN, and the proposed adaptive offline DNN‑fuzzy controller. High‑fidelity simulations under identical initial conditions were conducted to quantify performance. Results show that, compared with the PID controller (interception time ≈ 50 s, range ≈ 8000 m) and the adaptive offline DNN controller (35 s, 4600 m), the proposed controller intercepts the target in just 10.6 s at a range of about 610 m, corresponding to a 78.8% reduction in time-to-impact and a 92.4% reduction in range. These improvements enhance mission responsiveness, reduce target evasion opportunities, and broaden applicability to time-critical defense and high-speed interception scenarios. https://jast.ias.ir/article_230232.html Flight delays significantly affect airline operations and passenger satisfaction. While deep learning models such as long short-term memory (LSTM) and gated recurrent units (GRU) are promising for prediction, their performance is highly sensitive to hyperparameter configuration.This study proposes a novel hybrid deep learning framework integrated with particle swarm optimization (PSO) to enhance prediction accuracy. We develop a model that synergistically combines GRU and LSTM layers to capture short-term and long-term temporal dependencies in flight data. Importantly, the PSO meta-heuristic algorithm is employed to automate the optimization of key hyperparameters, including the number of units, dropout rate, learning rate, batch size, and training periods. The model was trained and tested on a comprehensive dataset of US airports, including features such as weather, air traffic volume, and operational details. Our results show superior performance with 98.24% accuracy, 94.00% precision, 96.00% recall, and 95.00% F1 score, significantly outperforming the baseline LSTM/GRU models and other methods reported in recent papers. This research emphasizes the critical role of systematic hyperparameter tuning and provides a robust and accurate tool for flight delay prediction, with potential applications in airline resource planning and real-time delay management. https://jast.ias.ir/article_235165.html Achieving high interception accuracy in Integrated Guidance and Control (IGC) for Autonomous Aerial Systems (AAS) is hindered by modeling uncertainties and system nonlinearities. While classical controllers like PID and LQR struggle with these dynamics, advanced robust controllers such as Sliding Mode Control (SMC) suffer from practical issues like chattering. This study investigates if a hybrid predictive control architecture can surpass both classical and robust nonlinear controllers in uncertain engagement scenarios. We hypothesize that augmenting a Model Predictive Controller (MPC) with an online neural-network (NN) based system identifier will enable adaptation to real-time dynamics, leading to superior performance. We propose an MPC-NN framework where an NN performs online parametric identification from sensor data, continuously updating the MPC’s internal prediction model at each sampling instant. The framework’s performance was rigorously benchmarked against PID, LQR, and a robust SMC baseline in 3D short-range air-defense scenarios using comprehensive Monte Carlo simulations. The proposed MPC-NN demonstrated decisive superiority. Under stochastic uncertainties, it achieved a mean terminal miss distance of 0.87 m, significantly outperforming SMC (1.98 m), LQR (7.91 m), and PID (18.45 m). The MPC-NN also proved more efficient, reducing interception time by 17% compared to SMC while consuming nearly half the peak control effort and completely avoiding actuator saturation—a problem prevalent in other controllers. These results validate our hypothesis, confirming that the adaptive MPC-NN architecture provides a transformative advantage in accuracy, speed, and efficiency. This framework represents a highly promising and practical solution for next-generation air-defense systems facing unpredictable, high-maneuverability threats. https://jast.ias.ir/article_240978.html This research introduces a novel framework for the modeling and optimization of a morphing wing bio-inspired by fish skeleton structures. The framework employs a hybrid machine learning model that synergistically combines Deep Q-Learning (DQL) with the Gray Wolf Optimizer (GWO) algorithm. The GWO metaheuristic efficiently searches for the optimal hyperparameters of the DQL neural network, which learns to predict aerodynamic coefficients. This integration enhances convergence speed, prevents entrapment in local minima, and significantly improves prediction accuracy. Once trained, the model provides real-time predictions of optimal wing geometry under variable flight conditions. Validation against experimental and numerical reference data demonstrates the superior accuracy of the proposed GWO-DQL approach compared to a standard DQL model. This work represents a significant step forward in integrating metaheuristic optimization with deep reinforcement learning for the design of intelligent, adaptive aerospace systems. The practical significance of this research is multifaceted regarding artificial intelligence. In this way, the trained model provides a powerful tool to reduce the reliance on costly (CFD) simulations and wind tunnel experiments by estimating aerodynamic responses in real-time. This capability is best visualized by the model's ability to generate comprehensive lift-drag polar curves for the entire operational envelope of the morphing wing. https://jast.ias.ir/article_251166.html Stability control of a quadrotor in hovering mode under random disturbances is a fundamental challenge in aerial robotics due to its highly nonlinear dynamics. In this paper, to enhance the robustness of the system against disturbances, two deep reinforcement learning algorithms — Deep Deterministic Policy Gradient (DDPG) and Deep Q-Network (DQN) — are employed to design intelligent controllers. In the proposed approach, the quadrotor dynamic model is implemented in the MATLAB simulation environment, and the performance of both intelligent agents in maintaining stability is compared considering a discrete action space. The main innovation of this study lies in analyzing the effect of action-space discretization on the performance of the DDPG algorithm and its comparison with DQN under random disturbances, a topic that has been rarely addressed in previous research. Simulation results show that both algorithms can maintain system stability; however, DDPG achieves better performance in terms of angular error reduction and smoother control response compared to DQN. The findings demonstrate the effectiveness of the proposed approach in improving the stability and adaptability of quadrotors under uncertain and disturbed conditions. https://jast.ias.ir/article_251167.html This study presents a robust Integrated Guidance and Control architecture tailored for nuclear-capable Intercontinental Ballistic Missiles (ICBMs) operating in hostile electronic warfare and GPS-denied environments. Addressing the critical challenge of maintaining precision under severe sensor noise and deliberate fault injection, the proposed solution synergizes an Adaptive Continuous Higher-Order Sliding Mode Controller with a predictive Long Short-Term Memory (LSTM) neural network. The LSTM module provides real-time compensation for inertial navigation errors, enabling the adaptive controller to ensure finite-time convergence and stability. Validation is conducted using a high-fidelity, industrial-grade six-degree-of-freedom simulation that incorporates variable mass/inertia dynamics, Mach-dependent aerodynamics, and realistic actuator constraints. Comparative simulations across four operational scenarios demonstrate that the proposed framework outperforms classical PID, sliding mode, and H∞controllers, achieving a 40% reduction in Circular Error Probable (CEP), a 30–35% decrease in settling time, and a 15% saving in energy consumption. Furthermore, parametric sensitivity analysis confirms robust performance under ±20% uncertainty. These findings establish the proposed architecture as a resilient and scalable solution for next-generation strategic missile guidance. https://jast.ias.ir/article_251168.html The rapid digital transformation of the aerospace industry has created a growing need for intelligent, data-driven systems capable of enhancing quality, safety, and regulatory compliance. This study explores the integration of Artificial Intelligence (AI) into Aerospace Quality Management Systems (AQMS) as a strategic pathway toward intelligent standardization and adaptive compliance mechanisms. Traditional quality management frameworks, such as AS9100 and ISO 9001, though effective, often face limitations in addressing the dynamic challenges of modern aerospace production particularly in handling large-scale data, predicting quality deviations, and ensuring continuous conformity with evolving regulatory standards. By leveraging machine learning, predictive analytics, and cognitive automation, AI can revolutionize quality assurance, enabling proactive detection of nonconformities, real-time decision support, and continuous process improvement. The research proposes a conceptual model illustrating how AI-based tools can be systematically embedded within Aerospace Quality Management Systems to strengthen traceability, audit accuracy, and regulatory responsiveness. Moreover, the paper discusses the implications of AI for intelligent standardization where standards evolve dynamically through data feedback and examines potential governance frameworks ensuring ethical and reliable AI use in aerospace quality domains. The findings emphasize that integrating AI not only enhances operational efficiency and product reliability but also establishes a foundation for smart regulatory ecosystems capable of self-monitoring and adaptive compliance in future aerospace systems. https://jast.ias.ir/article_251169.html The objective of this study is to develop a conceptual design framework for a liquid-propellant space launch vehicle at the early stages of design. In this framework, key mission parameters including mass, geometry, flight trajectory, propulsion performance, and aerodynamic conditions are optimized. To this end, a mass–energetic approach is adopted, which models the relationship between mission energy and system mass. For solving the continuous multi-objective optimization problem, the Teaching–Learning-Based Optimization (TLBO) algorithm is employed. Owing to its parameter-free nature, TLBO demonstrates efficient convergence behavior and robust solution stability. The results show that, using this design framework, a launch vehicle capable of delivering a 440 kg payload into a 300 km circular orbit is obtained with a total liftoff mass of 69,205 kg and a total length of 22.3 m. Comparison with reference methods reveals that the proposed model, while accounting for subsystem-level details, provides greater precision and flexibility in determining design parameters. The practical implication of this approach lies in its applicability during the preliminary design phase of space missions, contributing to reduced costs and improved design reliability. The main novelty of this work lies in integrating the TLBO algorithm with the mass–energetic design approach for conceptual space system design a combination that has received limited attention in previous research. https://jast.ias.ir/article_251170.html The fatigue life of metallic materials is significantly influenced by a combination of intrinsic and extrinsic factors, among which environmental conditions and loading characteristics play a critical role. While the effects of stress amplitude, microstructure, and surface finish have been extensively documented, the specific influence of loading frequency (particularly under varying environmental conditions) remains insufficiently explored. This study presents an experimental investigation into the fatigue behavior of Aluminum Alloy 1100 under both atmospheric and partial vacuum environments, focusing on the influence of loading frequency. Fatigue tests were conducted across a range of frequencies, intentionally selected to avoid resonance effects, thereby ensuring that the observed responses reflect the inherent influence of frequency rather than dynamic amplification. The results reveal a clear divergence in fatigue life between atmospheric and partial vacuum conditions, highlighting the substantial impact of environmental interaction. A predictive relationship was developed to estimate fatigue life as a function of loading frequency, offering a practical tool for design and life assessment. These findings contribute to a deeper understanding of frequency-dependent fatigue mechanisms and provide valuable insights for structural components subjected to cyclic loading in aerospace-relevant environments. Given the increasing utilization of aluminum alloys in orbital platforms, satellite structures, and high-altitude systems, the proposed framework assists in optimizing fatigue performance and extending service life under the reduced-pressure or partial vacuum conditions typical of aerospace applications. https://jast.ias.ir/article_251171.html This paper presents two adaptive controllers for vibration reduction in aircraft landing gear: a fuzzy adaptive controller (FAC) and a classic model reference adaptive controller (MRAC). The control input is the active force generated by the landing gear actuators. Both controllers are designed for high-order vibration models to decrease vibration amplitude and improve passenger comfort. A 6-DOF dynamical state-space model of the landing gear is considered. This model includes lumped uncertainties such as external disturbance, measurement errors, unmodeled dynamics, and parametric uncertainties. In both controllers, the parameters are adjusted to guarantee Lyapunov stability of the overall system. The fuzzy adaptive controller uses a Mamdani-type fuzzy inference system with a singleton fuzzifier and center of gravity defuzzification as a universal approximator. Compared to existing methods, the main advantages of the proposed approach are: 1) closed-loop stability is proven via Lyapunov analysis, 2) fuzzy parameters are adapted online without offline training, 3) tracking error and vibration converge to zero, and 4) robustness against lumped uncertainties is achieved. The results show approximately 20% improvement with the proposed FAC over the classic MRAC. The proposed method effectively absorbs vibrations and promotes passenger comfort under external disturbances. https://jast.ias.ir/article_251175.html This paper presents a strategic aerospace-oriented framework for integrating Artificial Intelligence (AI) with international quality standards to optimize organizational innovation, decision-making, and performance in high-reliability and complex environments. Leveraging aerospace principles such as system reliability, risk-based thinking, traceability, and lifecycle management, the proposed model positions AI as a central strategic leader that continuously analyzes operational, quality, and performance data to generate actionable recommendations. Organizational units, operating within a hierarchical aerospace governance structure, implement AI-driven guidance according to their risk tolerance, compliance requirements, and innovation priorities.Results from conceptual analysis and simulations indicate that synchronized deployment of AI and quality standards significantly enhances innovation adoption, operational robustness, process reliability, and service performance. The model reduces error rates, process variability, and resource inefficiencies, while improving predictive maintenance, risk assessment, and prioritization of innovation portfolios. Balanced AI adoption ensures optimal coordination across organizational units, whereas excessive reliance may reduce flexibility in mission-critical decision-making.This study offers a theoretical foundation and practical roadmap for aerospace managers and policymakers to implement AI-enabled quality standardization. By aligning AI capabilities with international standards within an aerospace-inspired systems architecture, organizations can achieve simultaneous improvements in innovation, operational reliability, and overall performance, while strengthening resilience and competitive advantage in technology-intensive and dynamic markets.