Tracking Flight Control of Quadrotor Based on Disturbance Observer

Chen, Mou; Xiong, Shixun; Wu, Qingxian

doi:10.1109/tsmc.2019.2896891

Cited by 133 publications

(56 citation statements)

References 39 publications

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“…Suppose that the disturbance

ω_{i} false(t false)

is composed of harmonics of different frequencies and generated by the following exogenous system [41]:

\begin{matrix} right left right left right left right left right left right left0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em3pt \\ ω_{i} (t) = \sum_{k = 1}^{M} Q_{k}^{i} S_{k}^{i} (t), {\dot{S}}_{k}^{i} (t) = U_{k}^{i} S_{k}^{i} (t), \end{matrix}

where

S_{k}^{i} false(t false)

is the disturbance auxiliary variable,

S_{k}^{i} false(t false)

\in

R^{s}

Q_{k}^{i} \in R^{s \times s}

, and

U_{k}^{i}

\in

R^{s \times s}

. Since

ω_{i} false(t false)

is composed of harmonic waves with different frequencies, then the disturbance can be estimated by designing the following disturbance observer [42]:

\begin{matrix} right left right left right left right left right left right left0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em3pt \\ {\dot{α}}_{k}^{i} (t) = & [U_{k}^{i} + E_{k}^{i} (B Q_{k}^{i})] {\overset{false^}{S}}_{k}^{i} \end{matrix}

…”

Section: Design Of Disturbance Observers and Cooperative Controllermentioning

confidence: 99%

Dynamic event‐triggered cooperative formation control for UAVs subject to time‐varying disturbances

Lili

2020

IET Control Theory & Appl

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“…Suppose that the disturbance

ω_{i} false(t false)

is composed of harmonics of different frequencies and generated by the following exogenous system [41]:

\begin{matrix} right left right left right left right left right left right left0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em3pt \\ ω_{i} (t) = \sum_{k = 1}^{M} Q_{k}^{i} S_{k}^{i} (t), {\dot{S}}_{k}^{i} (t) = U_{k}^{i} S_{k}^{i} (t), \end{matrix}

where

S_{k}^{i} false(t false)

is the disturbance auxiliary variable,

S_{k}^{i} false(t false)

\in

R^{s}

Q_{k}^{i} \in R^{s \times s}

, and

U_{k}^{i}

\in

R^{s \times s}

. Since

ω_{i} false(t false)

is composed of harmonic waves with different frequencies, then the disturbance can be estimated by designing the following disturbance observer [42]:

\begin{matrix} right left right left right left right left right left right left0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em3pt \\ {\dot{α}}_{k}^{i} (t) = & [U_{k}^{i} + E_{k}^{i} (B Q_{k}^{i})] {\overset{false^}{S}}_{k}^{i} \end{matrix}

…”

Section: Design Of Disturbance Observers and Cooperative Controllermentioning

confidence: 99%

Dynamic event‐triggered cooperative formation control for UAVs subject to time‐varying disturbances

Lili

2020

IET Control Theory & Appl

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“…with the approximation error 2 satisfying 2 ≤ E 2 ∈ R + . Next, we consider the velocity tracking error as e 2 = X 2 −X 2 (28) Taking derivative of e 2 giveṡ…”

Section: Robust Saturated Backstepping Tracking Controller Designmentioning

confidence: 99%

“…Although the authors of [21][22][23] investigated backstepping controllers based on a DO and demonstrated good tracking performance, the authors of [24][25][26] devised DO-based tracking sliding mode controllers. High gain observers and robust controllers to deal with the tracking problem have also been presented in [27,28]. However, all the control strategies in [21][22][23][24][25][26][27][28], especially with regard to the backstepping controllers, require full-state measurements that may not be possible due to insufficient sensor numbers and/or sensor faults.…”

Section: Introductionmentioning

confidence: 99%

“…High gain observers and robust controllers to deal with the tracking problem have also been presented in [27,28]. However, all the control strategies in [21][22][23][24][25][26][27][28], especially with regard to the backstepping controllers, require full-state measurements that may not be possible due to insufficient sensor numbers and/or sensor faults.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust Backstepping Trajectory Tracking Control of a Quadrotor with Input Saturation via Extended State Observer

Xuan-Mung

Hong

2019

Applied Sciences

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Quadrotor unmanned aerial vehicles have become increasingly popular in several applications, and the improvement of their control performance has been documented in several studies. Nevertheless, the design of a high-performance tracking controller for aerial vehicles that reliably functions in the simultaneous presence of model uncertainties, external disturbances, and control input saturation still remains a challenge. In this paper, we present a robust backstepping trajectory tracking control of a quadrotor with input saturation. The controller design accounts for both parameterized uncertainties and external disturbances, whereas a new auxiliary system is proposed to cope with control input saturation. Taking into account that only the position and attitude of the quadrotor are measurable, we devise an extended state observer to supply the estimations of unmeasured states, model uncertainties, and external disturbances. We strictly prove the stability of the closed-loop system by using the Lyapunov theory and demonstrate the effectiveness of the proposed algorithm through numerical simulations.

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“…The effectiveness of the proposed observer–controller scheme is verified through real‐time experiments. A state feedback controller based on a disturbance observer is proposed for trajectory tracking of the quadrotor in [16]. To check the effectiveness of the proposed control strategy, Quanser Qball 2 quadrotor is employed for real‐time experiments.…”

Section: Introductionmentioning

confidence: 99%

Finite‐time super twisting sliding mode controller based on higher‐order sliding mode observer for real‐time trajectory tracking of a quadrotor

Tripathi

Kamath

Behera

et al. 2020

IET Control Theory & Appl

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Tracking Flight Control of Quadrotor Based on Disturbance Observer

Cited by 133 publications

References 39 publications

Dynamic event‐triggered cooperative formation control for UAVs subject to time‐varying disturbances

Dynamic event‐triggered cooperative formation control for UAVs subject to time‐varying disturbances

Robust Backstepping Trajectory Tracking Control of a Quadrotor with Input Saturation via Extended State Observer

Finite‐time super twisting sliding mode controller based on higher‐order sliding mode observer for real‐time trajectory tracking of a quadrotor

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