Quadrotor control using feedback linearization with dynamic extension

Al-Hiddabi, S.A.

doi:10.1109/isma.2009.5164788

Cited by 43 publications

(32 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Examples of classical control approaches used to track trajectories with quadrocopters are PID schemes (Zhou et al 2010a), backstepping control techniques (Bouabdallah and Siegwart 2005;Mokhtari and Benallegue 2004;Zuo 2010;Madani and Benallegue 2006;Lee et al 2009;Raffo et al 2008) and feedback linearization (Zhou et al 2010b;Al-Hiddabi 2009). Other common strategies are trajectory linearization control (Zhu and Huo 2010), constrained finitetime optimal control (Alexis et al 2010), LQ optimal solutions (Bauer et al 2009) or Model Predictive Control (Castillo et al 2007).…”

Section: Related Workmentioning

confidence: 99%

Optimization-based iterative learning for precise quadrocopter trajectory tracking

2012

View full text Add to dashboard Cite

Current control systems regulate the behavior of dynamic systems by reacting to noise and unexpected disturbances as they occur. To improve the performance of such control systems, experience from iterative executions can be used to anticipate recurring disturbances and proactively compensate for them. This paper presents an algorithm that exploits data from previous repetitions in order to learn to precisely follow a predefined trajectory. We adapt the feed-forward input signal to the system with the goal of achieving high tracking performance-even under the presence of model errors and other recurring disturbances. The approach is based on a dynamics model that captures the essential features of the system and that explicitly takes system input and state constraints into account. We combine traditional optimal filtering methods with state-of-theart optimization techniques in order to obtain an effective and computationally efficient learning strategy that updates the feed-forward input signal according to a customizable learning objective. It is possible to define a termination condition that stops an execution early if the deviation from the nominal trajectory exceeds a given bound. This allows for a safe learning that gradually extends the time horizon of the trajectory. We developed a framework for generating arElectronic supplementary material The online version of this article (bitrary flight trajectories and for applying the algorithm to highly maneuverable autonomous quadrotor vehicles in the ETH Flying Machine Arena testbed. Experimental results are discussed for selected trajectories and different learning algorithm parameters.

show abstract

Section: Related Workmentioning

confidence: 99%

Optimization-based iterative learning for precise quadrocopter trajectory tracking

2012

View full text Add to dashboard Cite

show abstract

“…By the rotation of four rotor wings four rotor UAV can produce the control action. Among them, motor 1 M and motor 3 M have the clockwise rotations respectively with the angular velocity 1 ω and the angular velocity 3 ω to respectively generated the thrust 1 f and the thrust 3 f . At the same time, the other two motors ( 2 M and 4 M ) have the counter-clockwise rotations respectively with the angular velocity 2 ω and the angular velocity 4 ω to respectively generated the thrust 2 f and the thrust 4 f .…”

Section: Introductionmentioning

confidence: 99%

Study on Motion Mathematical Model of Four Rotor UAV

2017

2017 3rd International Conference on Environment, Biology, Medicine and Computer Applications (ICEBMCA 2017)

View full text Add to dashboard Cite

Four rotor UAV has the characteristics of small volume, simple construction, light weight, low cost and good invisibility. It can be adapted to multi-platform and multi-dimensional environment. It has the lower flight height and the very good mobility. It can have the flexible rising and landing on a relatively small platform. It doesn't need the ejection device, launcher and other auxiliary devices. Based on its many advantages, it has very broad application prospects both in military and civil fields. We mainly take the four rotor UAV as the research object to establish the kinematic and dynamic mathematical model of four rotor UAV and give the definition of usual coordinates for four rotor UAV, and then give the conversion relations of all vectors for four rotor UAV in inertial frame and body frame in the paper. We adopt Newton -euler method to obtain the rigid body dynamic equations of four rotor UAV model in the paper. We further analyze all kinds of forces and moments which act on the four rotor UAV in details again in the paper. We deduce the complete expression of overall mathematical model for four rotor UAV and make it be simplified in the paper.

show abstract

“…It is sometimes called a quadrotor or a qudrocopter. There have been many researches carried out on control and trajectory planning of quadcopters; refer to [1], [2], [3], [4], [5] and references therein.…”

Section: Introductionmentioning

confidence: 99%

“…The (differential) flatness property is useful for trajectory generation and the property of dynamic feedback linearizability makes tracking controller design straightforward. These two properties have been shown to hold for the quadcopter only locally in an open subset of the configuration space of the quadcopter dynamics [1], [3], [5], but not globally. In [3], [5] a flat output is constructed with the position and the yaw angle of the quadcopter and the consequent parameterization of the state and control variables in terms of the flat output has a singularity which naturally comes from the use of Euler angles.…”

Section: Introductionmentioning

confidence: 99%

Construction of an atlas for global flatness-based parameterization and dynamic feedback linearization of quadcopter dynamics

Chang

Eun

2014

53rd IEEE Conference on Decision and Control

View full text Add to dashboard Cite

We provide a global framework for flatness-based motion planning and dynamic feedback linearization of the quadcopter dynamics. It allows us to avoid the singularity difficulty that comes from the use of the yaw angle in flat output construction and dynamic feedback linearization. We construct eight differentially flat charts the union of which covers the entire configuration space of the quadcopter dynamics so that we can do global motion planning without encountering any singularity. In each differentially flat chart we transform the 12-dimensional quadcopter system via dynamic feedback to a 14-dimensional linear controllable system, which makes tracking controller design straightforward, so that we can switch from one controller to another to track a globally planned trajectory. The central theme of this paper is the global approach to the quadcopter motion planning and tracking.

show abstract

Quadrotor control using feedback linearization with dynamic extension

Cited by 43 publications

References 3 publications

Optimization-based iterative learning for precise quadrocopter trajectory tracking

Optimization-based iterative learning for precise quadrocopter trajectory tracking

Study on Motion Mathematical Model of Four Rotor UAV

Construction of an atlas for global flatness-based parameterization and dynamic feedback linearization of quadcopter dynamics

Contact Info

Product

Resources

About