High-level underactuated nonlinear control for rotorcraft machines

Brandão, Alexandre Santos; Sarcinelli-Filho, Mário; Carelli, Ricardo

doi:10.1109/icmech.2013.6518549

Cited by 35 publications

(23 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, the last important difference is the proposed nonlinear controller, which is based on a simplified mathematic model to represent the AR.Drone dynamics. Usually, other works are based on complex mathematic models to represent the quadrotor dynamics [10], [11], but our method shows that it is possible to get good results, in terms of control, with the AR.Drone in a simpler way.…”

Section: Introductionmentioning

confidence: 95%

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)

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 95%

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)

View full text Add to dashboard Cite

show abstract

“…Such a block corresponds to what is known as the High Level Model of the aircraft, and is out of the scope of this work. For details on how to deal with such a block, see [6], for instance.…”

Section: Introductionmentioning

confidence: 99%

Low Level Model Identification of a Quadrotor X3D-BL

Lopes

Sarcinelli-Filho

Brandão

et al. 2014

2014 Joint Conference on Robotics: SBR-LARS Robotics Symposium and Robocontrol

View full text Add to dashboard Cite

Resumo-This work aims at identifying the low level model of a miniature quadrotor, using a sensor of force and torque in the three axes of the Cartesian system. To do that, the first step is to obtain the low level model of the rotorcraft. Then we obtained the coefficient C f ,τ , which is a meaningful contribution of this work, since there are just a few works in the literature addressing such coefficient, necessary to convert the force and torque on the three axes in the individual strength of each propellant. As a continuation of this work, the parameters associated to the low level model here discussed will be also identified. Index Terms-Low-level modeling; Model identification; UAV model. I. INTRODUCTIONThe action of a controller capable of guiding an aircraft in predefined flight missions is one of the elements necessary for autonomous navigation. The design of such a controller often requires a model that suitably describes (following some criteria previously adopted) the behavior of the vehicle to be controlled. Many design techniques for flight controllers are based on the mathematical model of the aerial vehicle.Regarding the mathematical modeling of a small-scale aircraft, there are two well defined approaches in the reference literature: one based on the physical equations of the system and the other based on system identification techniques [1], [2]. Such approaches are not mutually exclusive: very often the use of one of them is required to simplify the other. In general, the first approach uses the equations of motion for the mechanical representation of a physical system, whereas the second one estimates the dynamic model of the physical system based on the data correspondent to excitation and response. According to [3] and [4], the complete model of a quadrotor (shown in Fig. 1) can be represented by four interconnected subsystems, as shown in Fig. 2 where u θ , u φ , uψ and uż are the excitation correspondent to the angles of pitch (θ ) and roll (φ ), the excitation correspondent to the yaw rate (ψ) and the excitation correspondent to the rate of vertical lift (ż), respectively. In turn, ω i and f i , for i = 1, 2, 3, 4, are the rotational velocities developed by each motor and the forces generated by them, respectively.The first two blocks shown in Fig. 2 are responsible for receiving control signals and generating the forces that will act on the aircraft, which are all normal to the plane b X − b Y that characterizes the frame b X − b Y − b Z attached to the body of the vehicle (see Fig. 1), whose origin is the center of mass

show abstract

“…The other two subsystems are the High-level Model, which describes the aircraft movement at the 3-D space. Details about the complete model can be found in [27].…”

Section: The Quadrotor Modelmentioning

confidence: 99%

“…A set of dynamic equations can describe the whole AR.Drone Parrot model (see [27]), i.e., a representation through a white box model. However, the parameter identification of each subsystem shown in Figure 2 requires the input signals, the rotor speeds, the thrust produced by each propeller and the 3-D pose of the UAV.…”

Section: The Experimental Setupmentioning

confidence: 99%

Spectrogram Analysis: An Approach to Identify The Quadrotor Thrust Model

Brandão¹,

Pizetta²,

Pizziolo³

et al. 2018

Preprint

View full text Add to dashboard Cite

Abstract:This paper deals with a non-contact method to identify the aerodynamic propeller parameters of the Parrot AR.Drone quadrotor. The experimental set consists in a camera recording the vehicle flights, the audio signal is extracted and is used a spectrogram analysis to estimates the propeller velocity. First, the aerial vehicle takes off and starts a hovering maneuver. The experiment is repeated with different additional masses attached to its rigid body. If the weight over the UAV increases/decreases, then the propeller must rotates faster/slower to produce a higher/lower thrust, and consequently, the sound frequency increases/decreases. Finally, this proposition is validated experimentally, and the estimated velocity is used to identify the quadrotor thrust parameters.

show abstract

High-level underactuated nonlinear control for rotorcraft machines

Cited by 35 publications

References 23 publications

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

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

Low Level Model Identification of a Quadrotor X3D-BL

Spectrogram Analysis: An Approach to Identify The Quadrotor Thrust Model

Contact Info

Product

Resources

About