Modeling and control of a quadrotor UAV

Walid, Mizouri; Najar, Slaheddine; Mohamed, Azah; Lamjed, Bouabdallah

doi:10.1109/sta.2014.7086762

Cited by 20 publications

(6 citation statements)

References 8 publications

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“…Where d is the drag coefficient, l is the distance between the motors and the center of gravity and rotor I is the rotor inertia, i Ω is the angular speed produced by the th i motor, and b is the thrust coefficient [7].…”

Section: Mathematical Modelmentioning

confidence: 99%

Modelling and Simulation of Quadrotor Using Model-Based Design

Zhang¹,

Zhang²

2022

HSET

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In the recent years, the control of Unmanned Aerial Vehicles (UAV) has become one of the most interesting fields of research, especially for quadrotor aircraft due to the simple structure, superior maneuverability and low cost. This paper presents a Model-Based approach to develop the mathematical model of quadrotor and establish a control method to meet the target of the actual flight. It employs Simulink to design the Proportional Integral Derivative (PID) controller for altitude and attitude loop of quadrotor, and uses Stateflow to implement control logic method for different flight conditions and states.

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Section: Mathematical Modelmentioning

confidence: 99%

Modelling and Simulation of Quadrotor Using Model-Based Design

Zhang¹,

Zhang²

2022

HSET

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“…The typical simulation choice for computation is MATLAB [6,7], which offers full freedom to define the dynamics of the UAV. However, the complexity of having to derive the whole flight dynamics often incurs oversimplifications such as only considering frontwind aerodynamics [8], or very basic PID flight controllers [9].…”

Section: Related Workmentioning

confidence: 99%

HIL Flight Simulator for VTOL-UAV Pilot Training Using X-Plane

et al. 2022

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With the increasing popularity of vertical take-off and landing unmanned aerial vehicles (VTOL UAVs), a new problem arises: pilot training. Most conventional pilot training simulators are designed for full-scale aircrafts, while most UAV simulators are just focused on conceptual testing and design validation. The X-Plane flight simulator was extended to include new functionalities such as complex wind dynamics, ground effect, and accurate real-time weather. A commercial HIL flight controller was coupled with a VTOL convertiplane UAV model to provide realistic flight control. A real flight case scenario was tested in simulation to show the importance of including an accurate wind model. The result is a complete simulation environment that has been successfully deployed for pilot training of the Marvin aircraft manufactured by FuVeX.

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“…The thrust is produced by six rotors, which are placed around its center of mass with six degrees of freedom. Based on Walid et al [17] and Moussid et al [18], the modelling for rigid bodies could be derived with the Euler-Lagrange method. The body frame of a hexarotor is described in Fig.…”

Section: Mathematical Model Of Hexarotormentioning

confidence: 99%

“…However, nonlinear control always requires a vast detail in UAV dynamics that complicate the implementation of the control design process. To overcome this problem, Walid et al [17] has implemented numerical simulations of a cascaded proportional-integral-differential (PID) controller for a quadrotor. The PID control scheme was also designed for a hexacopter by Moussid et al [18].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Network Control and Optimal Path Planning of UAV Surveillance System With Energy Consumption Prediction

Wai

Prasetia

2019

IEEE Access

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A surveillance system is one of the most interesting research topics for an unmanned aerial vehicle (UAV). However, the problem of planning an energy-efficient path for the surveillance purpose while anticipating disturbances and predicting energy consumptions during the path tracking is still a challenging problem in recent years. The optimal path planning and the disturbance rejection control for a UAV surveillance system are investigated in this paper. A trained and tested energy consumption regression model is used to be the cost function of an optimal path planning scheme, which is designed from a clustered 3D real pilot flight pattern with the proposed K-agglomerative clustering method, and is processed via A-star and set-based particle-swarm-optimization (S-PSO) algorithm with adaptive weights. Moreover, an online adaptive neural network (ANN) controller with varied learning rates is designed to ensure the control stability while having a reliably fast disturbance rejection response. The effectiveness of the proposed framework is verified by numerical simulations and experimental results. By applying the proposed optimal path planning scheme, the energy consumption of the optimal path is only 72.3397 Wh while the average consumed energy of real pilot flight data is 96.593Wh. In addition, the proposed ANN control improves average root-meansquare error (RMSE) of horizontal and vertical tracking performance by 49.083% and 37.50% in comparison with a proportional-integral-differential (PID) control and a fuzzy control under the occurrence of external disturbances. According to all of the results, the combination of the proposed optimal path planning scheme and ANN controller can achieve an energy-efficient UAV surveillance systems with fast disturbance rejection response. INDEX TERMS Unmanned aerial vehicle (UAV), optimal path planning, set-based particle-swarmoptimization (S-PSO), adaptive weights, adaptive neural network (ANN), varied learning rates.

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Modeling and control of a quadrotor UAV

Cited by 20 publications

References 8 publications

Modelling and Simulation of Quadrotor Using Model-Based Design

Modelling and Simulation of Quadrotor Using Model-Based Design

HIL Flight Simulator for VTOL-UAV Pilot Training Using X-Plane

Adaptive Neural Network Control and Optimal Path Planning of UAV Surveillance System With Energy Consumption Prediction

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