Disturbance Rejection Flight Control for Small Fixed-Wing Unmanned Aerial Vehicles

Liu, Cunjia; Chen, Wenhua

doi:10.2514/1.g001958

Cited by 64 publications

(40 citation statements)

References 26 publications

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“…In this section, the linearized longitudinal model of a small fixed-wing UAV with the assistance of DLC, together with models of actuators, i.e., the elevator, motor throttle and flap, are briefly introduced. Readers can refer to [2], [32] for the illustration of the longitudinal dynamics of UAV.…”

Section: Problem Formulationmentioning

confidence: 99%

“…[1 0 0 0; 0 1 0 0; 0 0 1 0; 0 0 0 0; 0 0 0 1], C p [1 0 0 0 0; 0 0 0 0 1]; u, w, q, θ and h are the forward body velocity, vertical body velocity, pitch rate, pitch angle and height, respectively; δ e , δ t and δ f are the control inputs generated by the elevator, motor throttle and flap, respectively; X, Z and M are the force and moment coefficients given their associated subscripts; g is the acceleration of gravity; any state denoted further with * represents the state at the linearization point of the model; d u , d w , d q and d h are the lumped effects of external disturbances and directly affect the system states u, w, q and h, respectively. Since most external disturbances in flight control systems, e.g., wind gusts, normally present much slower dynamics than UAV itself [2], d is therefore assumed to be unknown constant.…”

Section: A Uav Dynamicsmentioning

confidence: 99%

“…where D Y [u u (1) h h (1) h (2) ] T , D Z is the state of the zero dynamics, and T M and T N are transformation matrices. To endow this coordinate transformation with more physical significance, let D Z…”

Section: B Tracking Controller Design Via Dynamic Inversionmentioning

confidence: 99%

“…where D Yr [u r u (1) r h r h (1) r h (2) (3) r − A x(5,:)DX − B xd(5,:)d ; K u and K h are the controller gains.…”

Section: B Tracking Controller Design Via Dynamic Inversionmentioning

confidence: 99%

“…If the tracking objective has been achieved, R r is also available as a priori knowledge, which is directly related to the reference commands, i.e., R r = B a Q −1 [u (2) r − A x(2,1:…”

Section: B Generator and Estimator Of Optimal Desired Statesmentioning

confidence: 99%

See 4 more Smart Citations

On the Actuator Dynamics of Dynamic Control Allocation for a Small Fixed-Wing UAV With Direct Lift Control

Yan

Yang

Liu

et al. 2020

IEEE Trans. Contr. Syst. Technol.

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A novel dynamic control allocation method is proposed for a small fixed-wing unmanned aerial vehicle (UAV), whose flaps can be actuated as fast as other control surfaces, offering an extra way of changing the lift directly. The actuator dynamics of this kind of UAVs, which may be sluggish comparing to the UAV dynamics, should also be considered in the control design. To this end, a hierarchical control allocation architecture is developed. A disturbance observer based high-level tracking controller is first designed to accommodate the lagging effect of the actuators and to compensate the adverse effect of external disturbances. Then, a dynamic control allocator based on a receding-horizon performance index is developed, which forces the actuator state in the low-level to follow the optimised reference. Compared to the conventional control allocation method that assumes ideal actuators with infinite bandwidths, higher tracking accuracy of the UAV and better energy efficiency can be achieved by the proposed method. Stability analysis and high fidelity simulations both demonstrate the effectiveness of the proposed method, which can be deployed on different fixed-wing UAVs with flaps to achieve better performance.

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Section: Problem Formulationmentioning

confidence: 99%

Section: A Uav Dynamicsmentioning

confidence: 99%

Section: B Tracking Controller Design Via Dynamic Inversionmentioning

confidence: 99%

“…where D Yr [u r u (1) r h r h (1) r h (2) (3) r − A x(5,:)DX − B xd(5,:)d ; K u and K h are the controller gains.…”

Section: B Tracking Controller Design Via Dynamic Inversionmentioning

confidence: 99%

“…If the tracking objective has been achieved, R r is also available as a priori knowledge, which is directly related to the reference commands, i.e., R r = B a Q −1 [u (2) r − A x(2,1:…”

Section: B Generator and Estimator Of Optimal Desired Statesmentioning

confidence: 99%

See 3 more Smart Citations

On the Actuator Dynamics of Dynamic Control Allocation for a Small Fixed-Wing UAV With Direct Lift Control

Yan

Yang

Liu

et al. 2020

IEEE Trans. Contr. Syst. Technol.

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Fixed‐time backstepping distributed cooperative control for multiple unmanned aerial vehicles

Cui

Zhou

Jin³

2022

Asian Journal of Control

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This paper proposes a fixed‐time backstepping distributed cooperative control scheme based on fixed‐time extended state observer (FxTESO) for multiple unmanned aerial vehicles (UAVs). A fixed‐time ESO, which is convergent independently of initial conditions, is designed to estimate and compensate the external disturbances in tracking process. Moreover, to eliminate the “explosion of complexity” in the traditional backstepping architecture, a nonlinear first‐order filter is adopted to construct the distributed fixed‐time control scheme. Based on the local information of neighboring UAVs, a fixed‐time backstepping cooperative controller is designed. The proposed formation algorithm can be shown practicable for the UAV control system by using of Lyapunov stability theory and graph theory. Simulation results are given to demonstrate the effectiveness of the proposed control scheme.

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Model of Atmospheric Effects Onto a Group of Unmanned Aerial Vehicles

Belonozhko

Korolev

Chernyshev

et al. 2022

Lecture Notes in Networks and Systems

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Disturbance Rejection Flight Control for Small Fixed-Wing Unmanned Aerial Vehicles

Cited by 64 publications

References 26 publications

On the Actuator Dynamics of Dynamic Control Allocation for a Small Fixed-Wing UAV With Direct Lift Control

On the Actuator Dynamics of Dynamic Control Allocation for a Small Fixed-Wing UAV With Direct Lift Control

Fixed‐time backstepping distributed cooperative control for multiple unmanned aerial vehicles

Model of Atmospheric Effects Onto a Group of Unmanned Aerial Vehicles

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