Modelling and PID controller design for a quadrotor unmanned air vehicle

Salih, A. L.; Moghavvemi, Mahmoud; Mohamed, Haider A. F.; Gaeid, Khalaf Salloum

doi:10.1109/aqtr.2010.5520914

Cited by 203 publications

(78 citation statements)

References 5 publications

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“…Quadcopter UAV is defined as a small vehicle with four rotor-propeller sets distributed around its body [1]. It is a highly nonlinear, multi input multi output (MIMO), extremely coupled and underactuated system [2].…”

Section: Introductionmentioning

confidence: 99%

Modelling and Control of H-Shaped Racing Quadcopter With Tilting Propellers

Alkamachi¹,

Erçelebi²

2017

FU Mech Eng

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Section: Introductionmentioning

confidence: 99%

Modelling and Control of H-Shaped Racing Quadcopter With Tilting Propellers

Alkamachi¹,

Erçelebi²

2017

FU Mech Eng

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“…: Vertical Take-Off and Landing (VTOL), maneuvers and altitude stabilization [3,6], while others are focusing in the development of control algorithms. Many control algorithms have been developed for a Quadrotor, such as the classic Propositional Integral Derivative (PID) controller, Linear Quadratic (LQR) Algorithm, and Back stepping control algorithms [2,[7][8][9][10]. These classical controllers, i.e., the PID and LQR algorithms, however, are not suitable for a nonlinear systems, while, back stepping control algorithms had shown poor performance in terms of its robustness.…”

Section: Introductionmentioning

confidence: 99%

Optimization of a neural network based direct inverse control for controlling a quadrotor unmanned aerial vehicle

Heryanto

Wahab

Kusumoputro

2015

MATEC Web of Conferences

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Abstract. UAVs are mostly used for surveillance, inspection and data acquisition. We have developed a Quadrotor UAV that is constructed based on a four motors with a lift-generating propeller at each motors. In this paper, we discuss the development of a quadrotor and its neural networks direct inverse control model using the actual flight data. To obtain a better performance of the control system of the UAV, we proposed an Optimized Direct Inverse controller based on re-training the neural networks with the new data generated from optimal maneuvers of the quadrotor. Through simulation of the quadrotor using the developed DIC and Optimized DIC model, results show that both models have the ability to stabilize the quadrotor with a good tracking performance. The optimized DIC model, however, has shown a better performance, especially in the settling time parameter.

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“…The resultant forces of the weight, W, act at the mass centre of each element and point downwards towards the ground (WS1, WS2, WS3, WS4 and WSTR in Figure 2). Since the vehicle is considered symmetric, the translational drag (TD) is supposed to act at the centre of the structure and is proportional to the linear speed of it (Park et al 2005), (Patel et al, 2012), through a drag coefficient which usually has values between 0.0005 Ns/m and 0.01 Ns/m (Bouadi et al, 2011), (Salih et al, 2010) for quadrotors of similar dimensions. The aerodynamic force acting on the blades is provided by the pressure and shear distributions over the object's surface, both of them appear due to the relative displacement between the object and the surrounding airflow.…”

Section: Quadrotor Dynamic and Aerodynamic Behaviourmentioning

confidence: 99%

Quadrotor multibody modelling by vehiclesim: adaptive technique for oscillations in a PVA control system

Estelles

Tomás-Rodrı́guez

2015

Journal of Vibration and Control

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The work presented here covers the detailed modelling and trajectory control for an elastic bladed quadrotor vehicle. The benefits of using VehicleSim modelling software are also discussed. The authors present a full elastic structural and dynamical model as well as two different aerodynamic models. These two aerodynamic models differ from each other on their level of complexity and therefore, accuracy. The control methodology employed to stabilize and guide the vehicle is PVA (ProportionalVelocity-Acceleration), derived and implemented by using Simulink. As it will be shown, it stabilises and provides satisfactory quadrotor trajectory tracking.Since the control methodology feeds back the acceleration of the vehicle, and this acceleration has an oscillating nature, an adaptive process has been designed and introduced into the vehicle's model in order to avoid the oscillations' transmission to the control system, showing how it reduces the amplitude of the control actions oscillations.Results of simulations and discussion on them are also provided at the end of this article.

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Modelling and PID controller design for a quadrotor unmanned air vehicle

Cited by 203 publications

References 5 publications

Modelling and Control of H-Shaped Racing Quadcopter With Tilting Propellers

Modelling and Control of H-Shaped Racing Quadcopter With Tilting Propellers

Optimization of a neural network based direct inverse control for controlling a quadrotor unmanned aerial vehicle

Quadrotor multibody modelling by vehiclesim: adaptive technique for oscillations in a PVA control system

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