Modeling of Parrot Ardrone and passivity-based reset control

Falcon, Pablo; Barreiro, Antonio; Cacho, Miguel D.

doi:10.1109/ascc.2013.6606362

Cited by 9 publications

(7 citation statements)

References 12 publications

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“…Figure 1 displays the hexacopter and references frames defined. 24 However, as reported in several related works, such as in other works, [25][26][27][28][29] it is viable to analyze the multicopter response based on its internal controllers rather than dealing with its complex dynamics. This model has six outputs with four independent inputs, therefore, the hexacopter is an underactuated system.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…Hence, the hexacopter firmware takes into account the aerodynamic effects to achieve flight stabilization but, generally, developers cannot access the model parameters online. 24 However, as reported in several related works, such as in other works, [25][26][27][28][29] it is viable to analyze the multicopter response based on its internal controllers rather than dealing with its complex dynamics. According to Santana and Brandão, 25 the UAV can be modeled as a function of the high-level control signals.…”

Section: Mathematical Modelmentioning

confidence: 99%

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Identification and adaptive PID Control of a hexacopter UAV based on neural networks

Rosales

Soria

Rossomando

2018

Adaptive Control & Signal

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In this paper, a novel adaptive PID controller for trajectory-tracking tasks is proposed. It is implemented in discrete time over a hexacopter, and it takes into consideration the unmanned aerial vehicles (UAVs) nonlinear model. The PID controller is developed following an adaptive neural technique, and its stability is verified by the Lyapunov discrete theory. Besides, the neural identification of the dynamic model of the UAV is presented to backpropagate output errors to adjust PID gains with the purpose of reducing the control errors. The validation of the proposed algorithm is performed through experimental results with a hexacopter. KEYWORDSadaptive PID, discrete stability analysis, hexacopter, identification, neural networks 74 ;33:74-91. ROSALES ET AL. 75steady-state errors, and the derivative gain is used to modify any system overshoot. However, since quadrotor is a nonlinear underactuated systems, 8 it is not always possible to use PID control directly for the quadrotor system. Consequently, some authors proposed controllers designed based only on static PID for the UAV control such as the controllers presented in other works. [9][10][11] Other studies proposed the UAV control based on PID controllers with artificial intelligence and dynamic properties, such as the controllers presented in the work of Yang et al, 12 where authors defined a single-neuron PID, where the adjusting of the weights is a function of the control errors. Besides, simulations results were displayed to validate the proposal but the stability analysis is omitted. In addition, Xu and Zhou 13 use self-tuning PID controller based on adaptive pole placement to a quadrotor by using a linearized version of the dynamic model. Another class of PID controllers had the capabilities to backpropagate control errors for a better adjust of the controller gains, some of those are described in this work. Tsakalis and Dash 14 proposed an algorithm for  ∞ for online tuning of PID gains. The estimator is very complex to implement, although is robust with respect to the excitation spectrum. The algorithm is validated by using simulations results for different transfer functions. In the work of Liu et al, 15 the design of a Gaussian potential function network PID (GPFN-PID) controller based on GPFN NN was described. The proposed controller had the functions of online training, self-training, and self-adjusting, which made control of the UAV longitudinal channel more effective. However, the results obtained were based on simulations. Babu et al 16 proposed a gradient descent-based methodology for adjusting online the gains of a PID controller based on a cost function defined to penalize errors. The controller is designed only for positioning task and a commercial quadrotor is used to experiment the controller in position task. In the work of Wang et al, 17 an intelligent PID controller based on radial basis functions (RBFs) NN was designed for longitudinal attitude control of a small UAV using a nonlinear model. An RBF NN was utilized for online updatin...

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Section: Mathematical Modelmentioning

confidence: 99%

Section: Mathematical Modelmentioning

confidence: 99%

Identification and adaptive PID Control of a hexacopter UAV based on neural networks

Rosales

Soria

Rossomando

2018

Adaptive Control & Signal

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“…where • uż represents a linear velocity command, which causes displacements over the z w axis; • uψ represents an angular velocity command, which causes rotations around the z w axis; • u ϕ represents an inclination command related to the x w axis, which indirectly represents a command of linear velocity related to the y b axis [5], [14], [15], [16]. For this reason it will be hereinafter called u vy ; • u θ represents an inclination command related to the y w axis, which indirectly causes linear velocity related to the x b axis [5], [14], [15], [16].…”

Section: B Ardrone Motion Controlmentioning

confidence: 99%

“…For this reason it will be hereinafter called u vy ; • u θ represents an inclination command related to the y w axis, which indirectly causes linear velocity related to the x b axis [5], [14], [15], [16]. For this reason it will be hereinafter called u vx .…”

Section: B Ardrone Motion Controlmentioning

confidence: 99%

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A trajectory tracking and 3D positioning controller for the AR.Drone quadrotor

Santana

Brandão

Sarcinelli-Filho

et al. 2014

2014 International Conference on Unmanned Aircraft Systems (ICUAS)

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In this paper a complete framework is proposed, to deal with trajectory tracking and positioning with an AR.Drone Parrot quadrotor flying in indoor environments. The system runs in a centralized way, in a computer installed in a ground station, and is based on two main structures,namely a Kalman Filter (KF) to track the 3D position of the vehicle and a nonlinear controller to guide it in the accomplishment of its flight missions. The KF is designed aiming at estimating the states of the vehicle, fusing inertial and visual data. The nonlinear controller is designed with basis on a dynamic model of the AR.Drone, with the closed-loop stability proven using the theory of Lyapunov. Finally, experimental results are presented, which demonstrate the effectiveness of the proposed framework.

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