Real-time stabilization and tracking of a four rotor mini-rotorcraft

Castillo, Pedro; Dzul, A.; Lozano, Rogelio

doi:10.23919/ecc.2003.7086519

Cited by 60 publications

(63 citation statements)

References 4 publications

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“…Direct position control as proposed in many papers (see e.g. [3], [4]) is most often not necessary for vehicle guidance and position measurement or estimation is most often not accurate enough for direct feedback control.…”

Section: Vehicle Controller Designmentioning

confidence: 99%

“…For further considerations of modelling, we refer to [1], [2] and [4]. We consider an inertial frame and a body fixed frame whose origin is in the center of mass of the quadrotor as shown in Fig.…”

Section: Dynamic Model Of the Quadrotormentioning

confidence: 99%

“…There are some contributions in the literature that are concerned with control system design for quadrotor vehicles, see e.g. [1], [2], [3] and [4] to mention only a few. Many of the proposed control systems are based on a linearized model and conventional PID-or state space control while other approaches apply sliding-mode or H ∞ control.…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear and neural network-based control of a small four-rotor aerial robot

Voos

2007

2007 IEEE/ASME International Conference on Advanced Intelligent Mechatronics

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Abstract-Small four-rotor aerial robots, so called quadrotor UAVs, have an enormous potential for all kind of neararea surveillance and exploration in military and commercial applications. In addition, they offer the possibility to fly either in-or outdoor. However, stabilizing control and guidance of these vehicles is a difficult task because of the nonlinear dynamic behavior. This paper describes the development of a nonlinear vehicle control system based on a combination of state-dependent Riccati equations (SDRE) and neural networks. Some first simulation results underline the performance of this new control approach for the current realization.

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Section: Vehicle Controller Designmentioning

confidence: 99%

Section: Dynamic Model Of the Quadrotormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Nonlinear and neural network-based control of a small four-rotor aerial robot

Voos

2007

2007 IEEE/ASME International Conference on Advanced Intelligent Mechatronics

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“…When these angles are associated to Lyapunov functions, it is possible to control the helicopter roll, pitch, and yaw angles. Castillo et al (2004) Earl and D'Andrea (2004) developed a filter for estimating in real-time the roll, pitch, and yaw angles based on data from a gyroscope and a vision system fixed on ground. Tayebi and McGilvray (2004) proposed the use of retro-feeding controller based on quaternions for taking-off, hovering, and landing.…”

Section: Quadrotorsmentioning

confidence: 99%

In-flight collision avoidance controller based only on OS4 embedded sensors

Becker¹,

Sampaio²,

Bouabdallah³

et al. 2012

J. Braz. Soc. Mech. Sci. & Eng.

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“…Concerning controller design for small quadrotor UAVs, some solutions are already proposed in the literature, see e.g. [1], [2], [3], [4] and [5] to mention only a few. Many of the proposed control systems are based on a linearized model and conventional PID-or state space control while other approaches apply sliding-mode, H ∞ or SDRE control [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear tracking and landing controller for quadrotor aerial robots

Voos

Bou-Ammar

2010

2010 IEEE International Conference on Control Applications

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Abstract-Quadrotor UAVs are one of the most preferred type of small unmanned aerial vehicles because of the very simple mechanical construction and propulsion principle. However, the nonlinear dynamic behavior requires a more advanced stabilizing control and guidance of these vehicles. In addition, the small payload reduces the amount of batteries that can be carried and thus also limits the operating range of the UAV. One possible solution for a range extension is the application of a base station for recharging purpose even during operation. In order to increase the efficiency of the overall system further, a mobile base station will be applied here. However, landing on a moving base station requires autonomous tracking and landing control of the UAV. In this paper, a novel nonlinear autopilot for quadrotor UAVs is extended with a tracking and landing controller to fulfil the required task. First simulation and experimental results underline the performance of this new control approach for the current realization.

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Real-time stabilization and tracking of a four rotor mini-rotorcraft

Cited by 60 publications

References 4 publications

Nonlinear and neural network-based control of a small four-rotor aerial robot

Nonlinear and neural network-based control of a small four-rotor aerial robot

In-flight collision avoidance controller based only on OS4 embedded sensors

Nonlinear tracking and landing controller for quadrotor aerial robots

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