Attitude Free Position Control of a Quadcopter using Dynamic Inversion

Wang, Jian; Bierling, Thomas; Achtelik, Michael; Höcht, Leonhard; Holzapfel, Florian; Zhao, Weihua; Go, Tiauw Hiong

doi:10.2514/6.2011-1583

Cited by 21 publications

(8 citation statements)

References 1 publication

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“…In the context of control of multirotor vehicles, the typical modelling approach has been to use a hover or 'static' model, that is, the thrust and rotor torque are proportional to the square of the propeller rotation rate, and to neglect H-force, pitching and rolling moments [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. As shown in [15], this model breaks down when a quadrotor flies at moderate speeds.…”

Section: Literature Reviewmentioning

confidence: 99%

Computationally Efficient Force and Moment Models for Propellers in UAV Forward Flight Applications

Gill

D’Andrea

2019

Drones

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Two low-order, parametric models are developed for the forces and moments that a rotating propeller undergoes in forward flight. The models are derived using a first-principles-based approach, and are computationally efficient in the sense of being represented by explicit expressions. The parameters for the models can be identified either using supervised learning/grey-box fitting from labelled data, or can be predicted using only the static load coefficients (i.e., the hover thrust and torque coefficients). The second model is a multinomial model that is derived by means of a Taylor series expansion of the first model, and can be viewed as a lower-order lumped parameter model. The models and parameter generation methods are experimentally tested against 19 propellers tested in a wind tunnel under oblique flow conditions, for which the data is made available. The models are tested against 181 additional propellers from existing datasets.

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Section: Literature Reviewmentioning

confidence: 99%

Computationally Efficient Force and Moment Models for Propellers in UAV Forward Flight Applications

Gill

D’Andrea

2019

Drones

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“…However, one drawback of the backstepping scheme is the need of unmeasurable high‐order derivations of the system states. In and , researchers separate the dynamic system into an inner‐loop subsystem and an outer‐loop one. This control structure is much more suitable for rotorcrafts, but the stability of the closed‐loop system are not easily to be ensured.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear robust sliding mode control of a quadrotor unmanned aerial vehicle based on immersion and invariance method

Zhao

Xian

Zhang

et al. 2014

Intl J Robust & Nonlinear

130

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Summary We present an asymptotic tracking controller for an underactuated quadrotor unmanned aerial vehicle using the sliding mode control method and immersion and invariance based adaptive control strategy in this paper. The control system is divided into two loops: the inner‐loop for the attitude control and the outer‐loop for the position. The sliding mode control technology is applied in the inner‐loop to compensate the unmatched nonlinear disturbances, and the immersion and invariance approach is chosen for the outer‐loop to address the parametric uncertainties. The asymptotic tracking of the position and the yaw motion is proven with the Lyapunov based stability analysis and LaSalle's invariance theorem. Real‐time experiment results performed on a hardware‐in‐the‐loop‐simulation testbed are presented to validate the good control performance of the proposed scheme. Copyright © 2014 John Wiley & Sons, Ltd.

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“…The vehicle parameters of interest for the AGGA are maximum vehicle airspeed (V max ) and the estimated time from detection of disturbance to vehicle recovery (t control ). Since this variable was not available from the UTM vehicle partners, t control was set to a constant value of 1 second based on the settling/response times for multirotors documented in references [11], [22], [23], [24], [25], [26], [27]. The maximum vehicle speed for NASA UTM vehicle partners operating multirotor vehicles were used in this study.…”

Section: Methods 1: Algebraic-geometric Geo-fence Algorithm a Methodologymentioning

confidence: 99%

Feasibility of varying geo-fence around an unmanned aircraft operation based on vehicle performance and wind

D'Souza

Ishihara

Nikaido³

et al. 2016

2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC)

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Managing trajectory separation of unmanned aircraft is critical to ensuring accessibility, efficiency, and safety in low altitude airspace. The concept of a geo-fence has emerged as a way to manage trajectory separation. A geo-fence consists of distance buffers that enclose individual trajectories to identify a 'keep-in' region and/or enclose areas that identify 'keep-out' regions. The 'keep-in' geo-fence size can be defined as a static number or calculated as a function of vehicle performance characteristics, state of the airspace, weather, and other unforeseen events such as emergency or disaster response. Given that the fleet of Unmanned Aircraft Systems (UAS) operating in low altitude airspace will be numerous and non-homogeneous, calculating a 'keep-in' geo-fence will need to balance operational safety and efficiency. A recently tested UAS Traffic Management (UTM) prototype used a geo-fence size of 30 meters, horizontally and vertically, for every operation submitted. The goal of this work is to determine the feasibility of a generalized, simple algorithm that calculates geo-fence sizes as a function of vehicle performance and potential wind disturbances. The resulting geo-fence size could be smaller or larger because the vehicle performance in the presence of wind is considered, thus leading to trajectory separation that is safe and efficient.In this paper, two simplified methods were developed to determine the feasibility of calculating a geo-fence as a function of vehicle parameters and wind information. The first method calculates the geo-fence using basic vehicle parameters and wind sensor data in a set of algebraic-geometric equations. The second method models a generic PID control system that uses a simplified set of equations of motion for the plant and uses gain scheduling to account for wind disturbances. It was found that the Algebraic-Geometric Geo-fence Algorithm provides geofence sizes of approximately 15 meters horizontally and 5 meters vertically, which is much smaller than the UTM static value of 30 meters. In the PID Controller Geo-fence Algorithm it was found that the geo-fence size is further reduced to less than 5 meters, horizontally and vertically. These results reveal that implementing geo-fence calculations provide UTM with the ability to schedule and separate operations based on geofences that are dynamic to vehicle capability and environment, which is more efficient than using a single static geo-fence.

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Attitude Free Position Control of a Quadcopter using Dynamic Inversion

Cited by 21 publications

References 1 publication

Computationally Efficient Force and Moment Models for Propellers in UAV Forward Flight Applications

Computationally Efficient Force and Moment Models for Propellers in UAV Forward Flight Applications

Nonlinear robust sliding mode control of a quadrotor unmanned aerial vehicle based on immersion and invariance method

Feasibility of varying geo-fence around an unmanned aircraft operation based on vehicle performance and wind

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