Yazar "Ahmadi, Karim" seçeneğine göre listele

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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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    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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    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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    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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    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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    Modified adaptive discrete-time incremental nonlinear dynamic inversion control for quad-rotors in the presence of motor faults
    (Academic Press Ltd- Elsevier Science Ltd, 2023) Ahmadi, Karim; Asadi, Davood; Nabavi-Chashmi, Seyed-Yaser; Tutsoy, Önder
    Unmanned air vehicles are intrinsically non-linear, unstable, uncertain, and prone to a variety of faults, most commonly the motor faults. The main objective of this paper is to develop a faulttolerant control algorithm for the quadrotors with the motor faults. Accordingly, a novel adaptive modified incremental nonlinear dynamic inversion (MINDI) control is proposed to stabilize and control the quad-rotor with partial motor faults. The controller consists of a MINDI controller augmented with a discrete-time nonlinear adaptive algorithm. Since the incremental nonlinear dynamic inversion (INDI) algorithm is essentially based on the sensor measurements, it necessitates the angular rates differentiation and therefore amplifies the high-frequency noises produced by the gyroscopes. The application of derivative filters causes unavoidable internal state delays in the INDI structure. Henceforth, the performance of the controller developed for the unstable and uncertain quadrotors degrades considerably. To address this drawback, this paper proposes the MINDI controller which basically derives the angular accelerations from the angular moment estimations. Furthermore, to increase the robustness of the MINDI against motor faults, a discrete-time adaptive controller has been incorporated. The performance of the proposed controllers is verified both through the nonlinear simulations and testbed experiments. The results are compared with a recent efficient algorithm, which had been implemented on a quad-rotor model.
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    Nonlinear robust adaptive control of an airplane with structural damage
    (Sage Publications Ltd, 2020) Asadi, Davood; Ahmadi, Karim
    This article investigates the design of a novel nonlinear robust adaptive control architecture to stabilize and control an airplane in the presence of left-wing damage. Damage effect is modeled by considering the sudden mass and inertia changes, center of gravity, and aerodynamic variations. The novel nonlinear control algorithm applies a state predictor as well as the error between the real damaged dynamics and a virtual model based on the nominal aircraft dynamics in the control loop of the adaptive strategy. The projection operator is used for the purpose of robustness of the adaptive control algorithm. The stability of the proposed nonlinear robust adaptive controller is demonstrated applying the Lyapunov stability theory. The performance of the proposed controller is compared with two previous successful algorithms, which are implemented on the Generic Transport Model airplane to accommodate wing damage. Numerical simulations demonstrate the effectiveness and advantages of the proposed robust adaptive algorithm regarding two other algorithms of adaptive sliding mode and L adaptive control.
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    Robust Reduced Order Thau Observer With the Adaptive Fault Estimator for the Unmanned Air Vehicles
    (IEEE-Inst Electrical Electronics Engineers Inc, 2023) Tutsoy, Önder; Asadi, Davood; Ahmadi, Karim; Nabavi-Chashmi, Seyed-Yaser
    Developing fault detection and diagnoses algorithms for the unmanned air vehicles such as the quadrotors is challenging since they are intrinsically non-linear, time-varying, unstable, and uncertain. This paper develops a reduced order Thau observer by only considering the uncertain rotational dynamics, which are re-constructed as the dominant linear and non-linear for the design purpose. Therefore, the proposed Thau observer is just third order and can reveal a rotational state estimation error in the presence of the quadrotor faults. This paper also equips the proposed Thau observer with a simple online adaptive fault estimation law, which is able to recognize up to two faulty actuators instantly using the estimated rotational state error. Lyapunov analysis confirms the error convergence in both the Thau observer states and the adaptive fault estimates. In addition, this paper constructs a batch type least-squares projection approach to quantify the magnitude percentages of the actuator failures. Moreover, to show the feasibility of the proposed algorithm, this paper extensively analyses the fault detection and diagnosis results performed in the simulation and real-time environments. Finally, to demonstrate the superiority of the proposed algorithm, it is compared with a recent Kalman filter based quadrotor fault estimation research under the equal conditions.