Embedded Model Control for UAV Quadrotor via Feedback Linearization

Lotufo, Mauricio Alejandro; Colangelo, Luigi; Montenegro, Carlos Perez; Novara, Carlo; Canuto, Enrico

doi:10.1016/j.ifacol.2016.09.046

Cited by 16 publications

(11 citation statements)

References 10 publications

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“…In general, the main goal of control of a quadrotor is attitude and altitude control which keeps the quadrotor in the set point [19,20]. Therefore, some control approaches have been used for control of quadrotor including PID [21,22], Linear Quadratic Regulator (LQR) [23,24], feedback linearization control [25,26], backstepping control [27,28], SMC [29,30], and adaptive control [29,31]. SMC scheme is accepted as an efficient instrument for planning a robust controller of high-order systems with nonlinear and uncertain components [32,33].…”

Section: Background and Motivationsmentioning

confidence: 99%

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Adaptive Terminal Sliding Mode Control for Attitude and Position Tracking Control of Quadrotor UAVs in the Existence of External Disturbance

2021

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Section: Background and Motivationsmentioning

confidence: 99%

“…Considering ̃=̂− and substituting (28) into (36), we obtain ̇( ) =̃̇(̂− ) + ( )(̇( ) + ( ) +̈( )). (37) Now, the adaptation laws (33) are substituted into (37) where using (18), (25) and 27…”

Section: Proofmentioning

confidence: 99%

Adaptive Terminal Sliding Mode Control for Attitude and Position Tracking Control of Quadrotor UAVs in the Existence of External Disturbance

2021

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“…The scheme is completed by an appropriate selection of τ τ τ r in (22) ensuring that the attitude q tracks the desired attitude q d . This task is easily realizable because of Assumption 1, which guarantees the full-authority control action on the rotational dynamics.…”

Section: Controller Schemementioning

confidence: 99%

“…These are generally linear solutions based on proportionalderivative schemes or linear quadratic regulators, see, e.g., [2,20,31]. Hovering non-linear controllers are instead not equally popular and mainly exploit feedback linearization [3,22], sliding mode and backstepping techniques [1,5] and/or geometric control approaches [9,16].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical nonlinear control for multi-rotor asymptotic stabilization based on zero-moment direction

et al. 2020

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We consider the hovering control problem for a class of multi-rotor aerial platforms with generically oriented propellers. Given the intrinsically coupled translational and rotational dynamics of such vehicles, we first discuss some assumptions for the considered systems to reject torque disturbances and to balance the gravity force, which are translated into a geometric characterization of the platforms that is usually fulfilled by both standard models and more general configurations. Hence, we propose a control strategy based on the identification of a zero-moment direction for the applied force and the dynamic state feedback linearization around this preferential direction, which allows to asymptotically stabilize the platform to a static hovering condition. Stability and convergence properties of the control law are rigorously proved through Lyapunov-based methods and reduction theorems for the stability of nested sets. Asymptotic zeroing of the error dynamics and convergence to the static hovering condition are then confirmed by simulation results on a star-shaped hexarotor model with tilted propellers.

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“…Hence, the attitude and altitude stability problems are the primary goals for most research in this sector. Some of the methods used to control the quadrotor platform are the proportional-integral-derivative (PID) control [1][2][3], linear quadratic regulator (LQR) control [4][5][6], sliding mode control [7][8][9], feedback linearization control [10][11][12], fuzzy logic (FL) [13][14][15] and backstepping control [16][17][18]. In this paper, the quadrotor helicopter's stability issue is regarded.…”

Section: Introductionmentioning

confidence: 99%

Optimal backstepping control of quadrotor UAV using gravitational search optimization algorithm

Basri

Noordin

2020

Bulletin EEI

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Quadrotor unmanned aerial vehicle (UAV) has superior characteristics such as ability to take off and land vertically, to hover in a stable air condition and to perform fast maneuvers. However, developing a high-performance quadrotor UAV controller is a difficult problem as quadrotor is an unstable and underactuated nonlinear system. The effort in this article focuses on designing and optimizing an autonomous quadrotor UAV controller. First, the aerial vehicle's dynamic model is presented. Then it is suggested an optimal backstepping controller (OBC). Traditionally, backstepping controller (BC) parameters are often selected arbitrarily. The gravitational search algorithm (GSA) is used here to determine the BC parameter optimum values. In the algorithm, the control parameters are calculated using an integral absolute error to minimize the fitness function. As the control law is based on the theorem of Lyapunov, the asymptotic stability of the scheme can be ensured. Finally, several simulation studies are conducted to show the efficacy of the suggested OBC.

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Embedded Model Control for UAV Quadrotor via Feedback Linearization

Cited by 16 publications

References 10 publications

Adaptive Terminal Sliding Mode Control for Attitude and Position Tracking Control of Quadrotor UAVs in the Existence of External Disturbance

Adaptive Terminal Sliding Mode Control for Attitude and Position Tracking Control of Quadrotor UAVs in the Existence of External Disturbance

Hierarchical nonlinear control for multi-rotor asymptotic stabilization based on zero-moment direction

Optimal backstepping control of quadrotor UAV using gravitational search optimization algorithm

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