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    A review of control strategies used for morphing aircraft applications
    (Elsevier Science Inc, 2024) Parancheerivilakkathil, Muhammed S.; Pilakkadan, Jafar S.; Ajaj, Rafic M.; Amoozgar, Mohammadreza; Asadi, Davood; Zweiri, Yahya; Friswell, Michael I.
    This paper reviews the various control algorithms and strategies used for fixed -wing morphing aircraft applications. It is evident from the literature that the development of control algorithms for morphing aircraft technologies focused on three main areas. The first area is related to precise control of the shape of morphing concepts for various flight conditions. The second area is mainly related to the flight dynamics, stability, and control aspects of morphing aircraft. The third area deals mainly with aeroelastic control using morphing concepts either for load alleviation purposes and/or to control the instability boundaries. The design of controllers for morphing aircraft/wings is very challenging due to the large changes that can occur in the structural, aerodynamic, and inertial characteristics. In addition, the type of actuation system and actuation rate/ speed can have a significant effect on the design of such controllers. The aerospace community is in strong need of such a critical review especially as morphing aircraft technologies move from fundamental research at a low Technology Readiness Level (TRL) to real -life applications. This critical review aims to identify research gaps and propose future directions. In this paper, research activities/papers are categorized according to the control strategy used. This ranges from simple Proportional Integral Derivative (PID) controllers at one end to complex robust adaptive controllers and deep learning algorithms at the other end. This includes analytical, computational, and experimental studies. In addition, the various dynamic models used and their fidelities are highlighted and discussed. (c) 2023 Production and hosting by Elsevier Ltd. on behalf of Chinese Society of Aeronautics and Astronautics. This is an open access article under the CC BY -NC -ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
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    A unified and experimentally validated design framework for long-endurance solar UAVS using model-based multi-objective multidisciplinary optimization
    (Springer, 2025) Khaneghaei, Mohammad; Asadi, Davood; Ebrahimi, Benyamin; Hazeri, Majid; Farsadi, Touraj; Nabavi Chashmi, Yaser; Durhasan, Tahir
    Designing long-endurance, solar-powered unmanned aerial vehicles (UAVs) requires careful coordination across aerodynamic, structural, and energy subsystems, particularly when targeting flexible, high-aspect-ratio configurations. This paper presents a mission-driven design and optimization framework for solar-powered long-endurance UAVs, tailored to post-disaster urban surveillance scenarios. A modular, multidisciplinary approach is adopted to account for the coupled effects of structural deformation and solar energy availability, both of which critically affect flight endurance. A key feature of the framework is the simultaneous integration of aeroelastic constraints and a time-dependent solar power and battery model, capturing realistic energy generation and storage behavior over diurnal cycles. This energy model is experimentally validated using a custom-built testbed and incorporated directly into the design loop. The framework is implemented using a Multidisciplinary Design Optimization (MDO) architecture that employs a coupling strategy to effectively manage interdependencies among subsystems. A comprehensive sensitivity analysis using Latin Hypercube Sampling highlights key performance-driving parameters. The final UAV design is fabricated and flight-tested, demonstrating the satisfaction of mission-level requirements derived from a simulated post-earthquake damage assessment in Adana, T & uuml;rkiye. Battery state-of-charge, trajectory, and attitude data collected during flight tests demonstrate that the UAV operates in accordance with design predictions, despite environmental variability. The study highlights how the integration of validated subsystem models within an established optimization process can lead to reliable, application-specific solar UAV designs suitable for real-world deployment.
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    Active fault-tolerant control of quadrotor UAVs with nonlinear observer-based sliding mode control validated through hardware in the loop experiments
    (Pergamon-Elsevier Science Ltd, 2023) Ahmadi, Karim; Asadi, Davood; Merheb, Abdelrazzak; Yaser Nabavi-Chashmi, Seyed-; Tutsoy, Önder
    Multirotor unmanned aerial vehicles (UAV) are highly prone to motor faults, which can arise from defective motors or damaged propellers. Motor faults severely change the multirotor UAV's dynamics and therefore endanger flight safety and reliability since the controller loses its efficiency. To cope with such crucial problems, a fault-tolerant controller is proposed in this paper for full control of a quadrotor UAV with motor faults. The proposed fault-tolerant approach consists of the nonlinear observer technique and the Sliding Mode Control (SMC). The designed novel nonlinear observer predicts the effects of motor faults on quadrotor dynamics and it is augmented with an SMC to create a fault-tolerant controller. In addition, the nonlinear observer enhances the robustness of the SMC against the uncertainties and disturbances acting on the quadrotor during flight. Any actuator fault will be treated as a disturbance detected by the nonlinear observer and will be attenuated directly by the proposed SMC. The disturbance attenuation capability achieved by the nonlinear observer decreases the amount of control action expected from the SMC, which results in advanced robustness without sacrificing the nominal control performance. The performance of the proposed nonlinear observer SMC (NOSMC) is demonstrated through simulations and testbed experiments. The results show that the proposed fault-tolerant controller effectively recovers the full control of the quadrotor with motor faults up to 40% while tracking the predefined trajectory and rotation angles as desired.
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    Active flutter control of thin walled wing-engine system using piezoelectric actuators
    (Elsevier France-Editions Scientifiques Medicales Elsevier, 2020) Asadi, Davood; Farsadi, Touraj
    In the present study, control of a Thin Walled Beam (TWB) wing-engine system is examined applying piezoelectric actuators to enhance the performance of aeroelastic response. The composite piezoelectric governing equations of motion including the structure and electric effects are derived applying Hamilton's principle. Piezoelectric composite plate equations are added to the composite host wing-engine governing system of equations. The incompressible aerodynamic model based on Wagner's function is applied and the Ritz based solution methodology is employed. As a passive control approach, the effects of piezoelectric fiber angles are studied on the time domain response of the high-aspect-ratio wing-engine system. The linear quadratic Gaussian (LQG) control algorithm is applied as a closed-loop active control strategy to enhance the flutter response characteristics of the composite wing-engine system. Numerical results firstly, demonstrate the effectiveness of the active control strategy based on piezocomposite actuator on suppressing the flutter response of the composite wing-engine system and secondly, prove the effect of piezocomposite actuator location and fiber angle orientation on the closed-loop control performance. (C) 2020 Elsevier Masson SAS. All rights reserved.
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    Actuator Fault Detection, Identification, and Control of a Multirotor Air Vehicle Using Residual Generation and Parameter Estimation Approaches
    (Springer, 2024) Asadi, Davood
    Effective fault detection and identification (FDI) and fault-tolerant control for nonlinear, unstable, and underactuated systems like quadrotor is a challenging and critical process. This paper introduces a novel two-stage structure of an FDI approach integrated with an adaptive sliding mode controller for fault-tolerant control of a quadrotor with partial actuator fault. The FDI algorithm applies the parity space concept to generate a residual signal based on the system's states and the inputs. The residual signal is examined by the exponential forgetting factor recursive least square method to detect and identify the partial fault of the actuator. The cascade controller includes an adaptive SMC algorithm in the inner loop and a PID controller in the outer loop. Real-time testbed experiments and Monte-Carlo simulation are applied in different actuator fault scenarios to determine the FDI algorithm's performance metrics and demonstrate the effectiveness of the proposed algorithm. in maintaining full controllability of the quadrotor in presence of partial actuator fault.
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    Adaptive Hierarchical Multi-Headed Convolutional Neural Network With Modified Convolutional Block Attention for Aerial Forest Fire Detection
    (IEEE-Inst Electrical Electronics Engineers Inc, 2025) Mowla, Md. Najmul; Asadi, Davood; Masum, Shamsul; Rabie, Khaled
    Effective detection and classification of forest fire imagery are critical for timely and efficient wildfire management. Convolutional Neural Networks (CNNs) have demonstrated potential in this domain but encounter limitations when addressing varying scales, resolutions, and complex spatial dependencies inherent in wildfire datasets. Building upon our prior work on the Unmanned Aerial Vehicle-based Forest Fire Database (UAVs-FFDB) and the multi-headed CNN (MHCNN), this study introduces a novel architecture, namely, the Adaptive Hierarchical Multi-Headed Convolutional Neural Network with Modified Convolutional Block Attention Module (AHMHCNN-mCBAM). This enhanced framework addresses prior challenges by integrating adaptive pooling, concatenated convolutions for multi-scale feature extraction, and an improved attention mechanism incorporating shared fully connected layers, Glorot initialization, rectified linear units (ReLU), layer normalization, and attention-gating. AHMHCNN-mCBAM incorporates Gated Recurrent Unit (GRU) and Bidirectional Long Short-Term Memory (BiLSTM) networks for temporal context modeling to further refine classification accuracy. Experiments conducted on the UAVs-FFDB dataset achieved outstanding results, including 100% accuracy, a 100% Cohen's kappa coefficient (cKappa), and compact model parameter sizes of 1.49 million (M), 0.25 M, and 0.12 M. On the Fire Luminosity Airborne-based Machine Learning Evaluation (FLAME) dataset, the model attained accuracy rates of 99.83%, 99.10%, and 99.32%, with corresponding cKappa values of 99.66%, 98.20%, and 98.65%. Compared to the baseline hierarchical MHCNN with CBAM (HMHCNN-CBAM), AHMHCNN-mCBAM demonstrated significant performance gains, including a 6.80% and 6.59% increase in accuracy, a 9.26% and 14.11% improvement in cKappa, and a 13.87% and 13.76% reduction in parameter size on the UAVs-FFDB and FLAME datasets, respectively. Additionally, AHMHCNN-mCBAM outperformed HMHCNN-CBAM in recall (25% improvement), precision (21.95%), F1-score (14.94%), and fire detection rate (FDR) reduction (25.01%), while achieving a 100% reduction in error warning rate (EWR). Leveraging Explainable Artificial Intelligence (XAI) techniques, the model provides interpretable insights into decision-making processes.
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    Aerodynamic coefficient prediction of bio-inspired camber morphing wings with flexible surfaces using an explainable transformer
    (Elsevier France-Editions Scientifiques Medicales Elsevier, 2026) Mowla, Md Najmul; Durhasan, Tahir; Asadi, Davood; Kesilmi, Zehan; Jafari, Javad Rashid
    Bio-inspired morphing wings with flexible surfaces can enhance aerodynamic efficiency at low Reynolds numbers (Re), yet predicting their fluid-structure interaction remains challenging. We present PhysAero-MHANet, a physics-aware, interpretable deep learning framework coupled with controlled wind tunnel experiments for aerodynamic prediction of camber-morphing finite wings. The campaign yielded 911 samples spanning Re is an element of [3 x 10(4), 1 x 10(5)], camber deflections up to 10(degrees), and angles of attack from-18(degrees )to 18(degrees). Experiments showed up to 34% drag reduction at small angles of attack, a stall delay of approximate to 6(degrees), a maximum lift coefficient C-L,C-max approximate to 1.44, and a peak lift-to-drag ratio C-L/C-D approximate to 8.84. The proposed model is a transformer-based multi-task surrogate with physics-informed attention, hierarchical cross-feature fusion, and shapley additive explanations (SHAP) for interpretability. Against 11 machine-learning, deep-learning, and attention baselines, PhysAero-MHANet achieved R-2 approximate to 0.985 and MAPE < 12% across lift (C-L), drag (C-D), and rolling moment (C-M,C-R) predictions. These results provide new insight into morphing-wing aerodynamics and support real-time control, performance optimization, and integration into unmanned aerial vehicles (UAVs) and micro aerial vehicles (MAVs).
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    An experimental vision-based integrated guidance and control strategy for autonomous landing of a faulty UAV
    (SAGE Publications Ltd, 2025) Khaneghaei, Mohammad; Asadi, Davood; Zahmatkesh, Mohsen; Tutsoy, Onder
    Uncrewed Aerial Vehicles (UAVs) have emerged as a transformative asset in surveillance, mapping, and delivery tasks since they have sophisticated autonomous capabilities. This paper develops a practical vision-based optimal flight planning strategy for autonomous safe landing and control of a multirotor UAV using a low-cost monocular camera in the presence of a motor fault. An optimal integrated guidance and control strategy is developed by utilizing an innovative discrete system model and a state observer from the triggering point to the identified landing position. Additionally, compatible image processing techniques and UAV kinematics are integrated to detect the suitable landing site and translate its location into desired attitude inputs to the controller. This approach empowers the UAV to autonomously land in no-GPS environments, relying solely on camera data. Initially, vision-based sensors, image processing techniques, and the developed guidance and control algorithms undergo initial evaluation in Software in the Loop (SIL) simulations using the Robot Operating System (ROS) and Gazebo simulation environments. The efficacy of the proposed framework is then assessed through experimental flight tests across various landing scenarios, accounting for local wind conditions and motor faults.
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    Automatic Landing Control of a Multi-Rotor UAV Using a Monocular Camera
    (Springer, 2022) Nabavi, Yaser; Asadi, Davood; Ahmadi, Karim
    The reliability of autonomous landing of the UAVs in an unknown or unprepared environment can be improved by the application of image-based sensors. This paper investigates the landing control of a multirotor UAV by controlling the optical flow and estimation of vertical distance supported by a low-cost monocular camera. Landing control and vertical distance estimation using just a camera as the sensor, makes the proposed approach well-suited for emergency scenarios in GPS denied environment. To develop the optical flow-based controller strategy, an appropriate nonlinear model is proposed by combining the optical flow equations and the kinematics of vertical landing. In the control loop of the multirotor landing, an estimation of the vertical distance is required, which is provided by the application of the developed model and the optical flow derived from camera images. RLS algorithm and the EKF have been applied to estimate the vertical distance. The controller output is determined by using the NDI algorithm, the augmented state-space form of the equations, and the estimated states based on optical flow. An experimental setup is developed for the tasks of optical flow extraction, vertical distance estimation, and control. Additionally, the developed estimation and controller strategy are applied to the nonlinear dynamics of a quadrotor to demonstrate the applicability of the proposed models and algorithms for landing control. According to the results, the proposed optical flow-based control strategy can support a smooth landing for the multirotor UAV while its performance is dependent on the quality of estimation. There is a steady-state error in tracking the optical flow due to the estimation error of the vertical distance. The EKF-based algorithm has better performance in terms of estimation accuracy respecting the RLS, and therefore supports a better landing performance.
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    Experimental motor fault detection and identification of a quadrotor UAV using a hybrid deep learning approach
    (Springer Nature, 2025) Khaneghaei, Mohammad; Asadi, Davood; Mowla, Md. Najmul; Disken, Gokay
    This study presents a novel experimental hybrid sequential deep learning (DL) approach for real-time motor fault detection and magnitude estimation in quadrotor UAVs, addressing critical gaps in current fault-tolerant control systems. The proposed framework integrates long short-term memory (LSTM) networks with 1D convolutional neural networks (1D-CNN) to enhance fault classification and estimation accuracy. The dual capability distinguishes the proposed model from existing methods, which often focus solely on fault detection without addressing magnitude estimation. A novel dataset, generated through Hardware-in-the-Loop (HIL) experiments, incorporates 25,000 unique fault scenarios under diverse configurations and flight conditions. This dataset strengthens the model's robustness and applicability to real-world scenarios. The developed architecture demonstrated superior performance, achieving 99.2% fault detection accuracy, surpassing existing methods in robustness and efficiency. By embedding the model into a dynamic HIL testbed, the study validates the framework's capability to detect faults, estimate magnitudes, and restore stability in quadrotors under challenging conditions. Experimental results highlight the system's effectiveness in reducing motor anomalies, ensuring improved operational safety and reliability. The approach is adaptable to broader UAV systems, offering significant advancements in autonomous fault-tolerant control.
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    Fault-tolerant Trajectory Tracking Control of a Quadcopter in Presence of a Motor Fault
    (Springer, 2022) Asadi, Davood; Ahmadi, Karim; Nabavi, Seyed Yaser
    As a part of emergency landing architecture for multi-rotor, a fault-tolerant trajectory tracking control strategy is proposed in this paper to control a quadcopter in case of a partial motor fault. The introduction of fault-tolerant strategy includes a lightweight fault detection and identification algorithm and a three-loop tracking controller. The lightweight fault detection and identification algorithm identifies the fault based on the controller outputs and the angular rates calculated by a discrete extended Kalman filter. The three-loop controller comprises a cascade structure of a discrete nonlinear adaptive algorithm in the inner-loop and a PID algorithm in the outer-loops of the controller structure. To have more realistic simulations, the gyroscopic effects of rotors and the airframe drag terms are considered in modeling as the model uncertainty. The simulation results demonstrate that the proposed fault-tolerant controller can effectively control the quadcopter in presence of partial motor fault, model uncertainties, and sensor noises. The results also demonstrate the effect of fault detection time delay on the overall control performance.
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    Flutter improvement of a thin walled wing-engine system by applying curvilinear fiber path
    (Elsevier France-Editions Scientifiques Medicales Elsevier, 2019) Farsadi, Touraj; Asadi, Davood; Kurtaran, Hasan
    In the present study, the aeroelastic behavior of a wing-engine system modeled as composite Thin Walled Beam (TWB) with curvilinear fiber path is investigated. The variable stiffness is acquired by constructing laminates of TWB with curvilinear fibers having prescribed paths. In order to account the effect of chordwise and spanwise locations, mass, and thrust force of engine on the aeroelastic characteristics of TWB, the novel governing equations of motion are obtained using Hamilton's variational principle. The paper aims to exploit desirable fiber paths with improved aeroelastic properties for different wing-engine configuration. Ritz based solution methodology is employed to solve the equations with coupled incompressible unsteady aerodynamic model based on Wagner's function. Numerical simulation results which conform to previously published literatures are presented for validation purposes. Although different curvilinear fiber paths can be introduced to enhance flutter instabilities for each wing-engine configurations, there exists an ideal placement of engine on the wing considering only the engine mass, and the engine mass and thrust force, simultaneously. A comprehensive insight is provided over the effect of parameters such as the lamination fiber path and the effect of engine positions with different mass and thrust values on the flutter speed and frequency. (C) 2019 Elsevier Masson SAS. All rights reserved.
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    Flutter Optimization of a Wing-Engine System with Passive and Active Control Approaches
    (Amer Inst Aeronautics Astronautics, 2021) Asadi, Davood; Farsadi, Touraj; Kayran, Altan
    In the present study, the flutter performance of a composite thin-walled beam wing-engine system is optimized by implementing two different control approaches: 1) passive open-loop and 2) active closed-loop control. Sequential quadratic programming and genetic algorithm methods are applied in the optimization process. In the passive control method, variable stiffness is acquired by constructing laminates of thin-walled beam with curvilinear fibers having prescribed paths. The goal is to exploit the desirable fiber paths with improved flutter performance to determine an optimized wing-engine aeroelastic configuration. In the active control strategy, piezo-composite actuators and the linear quadratic Gaussian algorithm are used to improve the flutter characteristics. A novel optimization strategy based on the total energy of the aeroelastic system is introduced and applied in both passive and active control strategies. The minimum total aeroelastic energy is an indication of ideal optimization variables, which leads to optimum flutter performance. The governing equations are formulated based on Librescu's thin-walled beam theory and Hamilton's principle. An unsteady aerodynamic model based on incompressible indicial aerodynamics is applied. The governing equations of motion are solved using a Ritz-based solution methodology. Numerical results demonstrate a 16 and 46% improvement in the flutter speed of the wing-engine system using the proposed passive and active control approaches, respectively. The presented results provide valuable information concerning the design of advanced lightweight and high-aspect-ratio aircraft wings with mounted engines in terms of favorable aeroelastic performance characteristics.
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    Forecasting near-surface air temperature via SARIMA and LSTM: A regional time-series study
    (Pergamon-Elsevier Science Ltd, 2025) Aksoy, Muhammed M.; Mowla, Najmul; Bilgili, Mehmet; Pinar, Engin; Durhasan, Tahir; Asadi, Davood
    Accurate modeling of near-surface air temperature (AT) trends is critical for assessing global and regional climate risks, particularly in light of the intensifying warming signals observed across the northern hemisphere and the tropics. This study proposes a robust and computationally efficient framework for forecasting near-surface AT across the global, the northern hemisphere, the southern hemisphere, and the tropics using two complementary time-series modeling techniques: seasonal autoregressive integrated moving average (SARIMA) and long short-term memory (LSTM) networks. The models are trained to capture both structured seasonal patterns and nonlinear temporal dynamics by leveraging the ERA5 reanalysis dataset (1970-2024) and incorporating preprocessing steps such as detrending and Z-score normalization. SARIMA consistently outperformed LSTM across most domains, particularly in the global region, achieving lower RMSE (0.0967 degrees C) and higher correlation (R = 0.9975), reflecting its superior capacity for linear and seasonal signal extraction. Quantitatively, SARIMA demonstrates 5%-10% lower RMSE and slightly higher correlation than LSTM across all domains, underscoring the statistical significance of its performance advantage. Projected near-surface AT anomalies by 2050 reveal a marked warming trend, with the SARIMA model estimating a global anomaly of +1.078 degrees C and a northern hemisphere anomaly of +1.474 degrees C, closely aligning with IPCC-reported trajectories and exceeding CMIP5 RCP4.5 projections. The findings underscore SARIMA's reliability for short-to mid-term near-surface AT forecasting and LSTM's potential for future hybrid modeling schemes. This work fills a critical methodological gap by integrating statistical rigor with scalable deep learning, offering enhanced fidelity for regional climate adaptation planning.
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    Fundamental frequency optimization of variable stiffness composite skew plates
    (Springer Wien, 2021) Farsadi, Touraj; Asadi, Davood; Kurtaran, Hasan
    In this study, natural frequencies and vibrational mode shapes of variable stiffness composite skewed plates are optimized applying a genetic algorithm. The variable stiffness behavior is obtained by altering the fiber angles continuously according to two selected curvilinear fiber path functions in the composite laminates. Fundamental frequency and related mode shapes of the plates are optimized for two different fiber path functions using the structural model obtained based on the virtual work principle. A three-layer composite skewed plate with four types of boundary conditions and different plate geometries is considered as case study in this research. Diverse sweptback angles as well as different aspect ratios are considered as various plate geometries. The present study aims to calculate the best fiber path with maximized fundamental frequency or in-plane strengths for a composite skewed plate. The generalized differential quadrature method of solution is employed to solve the governing equations of motion. Moreover, the linear kinematic strain assumptions are used, and the first-order shear deformation theory is employed to generalize the formulation for the case of moderately thick plates including transverse shear effects. Numerical results demonstrate the effect of the fiber angles, boundary conditions, and diverse geometries on the natural frequencies of the composite plate. The optimal fiber angles of each layer are presented for the above cases in free vibration analysis. It is verified that the application of optimized curvilinear fibers instead of the traditional straight fibers introduces a higher degree of flexibility, which can be used to adjust frequencies and mode shapes.
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    Image-based UAV position and velocity estimation using a monocular camera
    (Pergamon-Elsevier Science Ltd, 2023) Nabavi-Chashmi, Seyed-Yaser; Asadi, Davood; Ahmadi, Karim
    Autonomous landing of aerial vehicles is challenging, especially in emergency flight scenarios in which precise information about the vehicle and the environment is required for near-to-ground maneuvers. In this paper, the optic-flow concept based on feature detection is applied to estimate the vertical distance and the velocity vector of a multirotor UAV (MUAV) for landing. The UAV kinematics, the optical flow equations, and the detected feature states, provided by a low-cost monocular camera, are combined to develop a novel appropriate model for estimation. The proposed algorithm applies the variation of detected features, the angular velocities, as well as the Euler angles, measured by the Inertial Measurement Unit (IMU), to estimate the vertical distance of the UAV to the ground, the MUAV velocity vector, and also to predict the future features position. Extended Kalman filter (EKF) is applied as the estimation method on the coupled optic-flow and kinematic equations. The accuracy of state estimation is enhanced by the idea of multiple-feature tracking. The 6-DOF simulations, laboratory experiments, and comparison of results demonstrate the capability of height and velocity estimation of a MUAV in the landing phase of flight by just applying the low-cost camera information. Monte Carlo simulations have been performed to study the effect of IMU acceleration, and angular velocity measurement noises as well as the number of the detected features on the success probability of the estimation process. The results reveal that increasing the number of detected features, i.e tracking multiple features, increases the estimation accuracy, however, it mainly improves the success probability, which is a more important factor in practical scenarios.
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    INNOVATIVE SYNERGIES IN AIRCRAFT PROPULSION: THE CONCEPT OF HYBRID POWER SYSTEMS WITH CONTRA-ROTATING PROPELLERS
    (American Society of Mechanical Engineers (ASME), 2024) Hazeri, Majid; Moradkhani, Mohsen; Jafari, Javad Rashid; Asadi, Davood
    In an era characterized by escalating emphasis on fuel economy and the mitigation of greenhouse gas emissions within the aerospace industry, this paper presents an innovative paradigm including the hybrid electric engine with contra-rotating propellers. This article unveils a pioneering technological achievement, exemplified by our patented invention registered under the identifier IB/2021/060538, which received a gold medal at the ICAN 2022 International Invention competition in Toronto, Canada, represents a noteworthy advancement in the domain of hybrid engine technology. It is imperative to acknowledge that the concept is currently in the conceptual design phase, necessitating further refinement to attain its maximum potential. The engine, characterized as a contra-rotating propeller system, engenders an efficiency gain ranging from 6% to 16% relative to single-fuel engines, with one internal combustion engine providing half of the required power and the electric motor complementing the remaining share. This innovative system comprises two distinct configurations: a system with two electric motors and one fuel engine in which one of the electric engines is used as a backup engine. In case of user preference or fuel engine failure, the backup electric is engaged in place of the fuel engine. This paradigm-shifting innovation effectively changes the conventional internal combustion engine into a multi-engine anti-torque system, facilitating augmented thrust generation while simultaneously reducing fuel consumption by an impressive margin of 40% to 60% when compared with conventional engine models. Beyond its commendable fuel efficiency, the hybrid engine is characterized by a satisfactory level of reliability. This is related to the inclusion of a backup electric motor. In addition to the internal combustion engine, supporting the system with the ability to manage system failures and maintain power output even under emergency circumstances. Notably, the fundamental concept of the contra-rotating propeller system is not entirely novel, however, our innovative approach harmoniously synchronizes two electric motors, thereby containing the advantages inherent in the contra-rotating system with the reliability attributed to electric propulsion. Copyright © 2024 by ASME.
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    INNOVATIVE SYNERGIES IN AIRCRAFT PROPULSION: THE CONCEPT OF HYBRID POWER SYSTEMS WITH CONTRA-ROTATING PROPELLERS
    (Amer Soc Mechanical Engineers, 2024) Hazeri, Majid; Moradkhani, Mohsen; Jafari, Javad Rashid; Asadi, Davood
    In an era characterized by escalating emphasis on fuel economy and the mitigation of greenhouse gas emissions within the aerospace industry, this paper presents an innovative paradigm including the hybrid electric engine with contra-rotating propellers. This article unveils a pioneering technological achievement, exemplified by our patented invention registered under the identifier IB/2021/060538, which received a gold medal at the ICAN 2022 International Invention competition in Toronto, Canada, represents a noteworthy advancement in the domain of hybrid engine technology. It is imperative to acknowledge that the concept is currently in the conceptual design phase, necessitating further refinement to attain its maximum potential. The engine, characterized as a contrarotating propeller system, engenders an efficiency gain ranging from 6% to 16% relative to single-fuel engines, with one internal combustion engine providing half of the required power and the electric motor complementing the remaining share. This innovative system comprises two distinct configurations: a system with two electric motors and one fuel engine in which one of the electric engines is used as a backup engine. In case of user preference or fuel engine failure, the backup electric is engaged in place of the fuel engine. This paradigm-shifting innovation effectively changes the conventional internal combustion engine into a multi-engine anti-torque system, facilitating augmented thrust generation while simultaneously reducing fuel consumption by an impressive margin of 40% to 60% when compared with conventional engine models. Beyond its commendable fuel efficiency, the hybrid engine is characterized by a satisfactory level of reliability. This is related to the inclusion of a backup electric motor. In addition to the internal combustion engine, supporting the system with the ability to manage system failures and maintain power output even under emergency circumstances. Notably, the fundamental concept of the contra-rotating propeller system is not entirely novel, however, our innovative approach harmoniously synchronizes two electric motors, thereby containing the advantages inherent in the contra-rotating system with the reliability attributed to electric propulsion.
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    Minimum Distance and Minimum Time Optimal Path Planning With Bioinspired Machine Learning Algorithms for Faulty Unmanned Air Vehicles
    (IEEE-Inst Electrical Electronics Engineers Inc, 2024) Tutsoy, Önder; Ahmadi, Karim; Asadi, Davood; Nabavi-Chashmi, Seyed Yaser; Iqbal, Jamshed
    Unmanned air vehicles operate in highly dynamic and unknown environments where they can encounter unexpected and unseen failures. In the presence of emergencies, autonomous unmanned air vehicles should be able to land at a minimum distance or minimum time. Impaired unmanned air vehicles define actuator failures and this impairment changes their unstable and uncertain dynamics; henceforth, path planning algorithms must be adaptive and model-free. In addition, path planning optimization problems must consider the unavoidable actuator saturations, kinematic and dynamic constraints for successful real-time applications. Therefore, this paper develops 3D path planning algorithms for quadrotors with parametric uncertainties and various constraints. In this respect, this paper constructs a multi-dimensional particle swarm optimization and a multi-dimensional genetic algorithm to plan paths for translational, rotational, and Euler angles and generates the corresponding control signals. The algorithms are assessed and compared both in the simulation and experimental environments. Results show that the multi-dimensional genetic algorithm produces shorter minimum distance and minimum time paths under the constraints. The real-time experiments prove that the quadrotor exactly follows the produced path utilizing the available maximum rotor speeds.
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    Model-based Fault Detection and Identification of a Quadrotor with Rotor Fault
    (Springer, 2022) Asadi, Davood
    Fault detection and identification (FDI) is a challenging and critical process when dealing with nonlinear, unstable, and underactuated systems such as multirotor. This article presents a novel two-stage structure for a fault-tolerant FDI approach for a quadrotor with an actuator fault. The FDI algorithm generates residual signals for fault detection using a model-based approach based on the parity space. The basic idea behind this approach is to leverage measurement coherence by generating residuals via linear combinations of measurement outputs and control inputs over a finite window. The parity vector is generated using the states of the faulty system, which have been filtered using an extended Kalman filter, the inputs, and the healthy quadrotor model. To detect and identify the actuator's partial fault, the residual signal is examined using the exponential forgetting factor recursive least square method. Real-time testbed experiments are used to determine the FDI algorithm's performance and to demonstrate the proposed algorithm's effectiveness in identifying a quadrotor's rotor fault.