Adaptive neural network finite time control for quadrotor UAV with unknown input saturation

Xu, Qingzheng; Wang, Zhisheng; Zhen, Ziyang

doi:10.1007/s11071-019-05301-1

Cited by 90 publications

(45 citation statements)

References 57 publications

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“…There are several control techniques for the attitude and altitude of the quadcopter such as proportional-integralderivative (PID) control [1,2], adaptive control [3][4][5], neural network [6,7], LQR control [8,9], model predictive control [10,11], have been investigated in many studies. Comparison with other approaches, sliding mode control (SMC) [12,13] is exploited as a special and robust control algorithm against parametric uncertainties, external disturbances through its sliding surface.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Sliding Mode Control for Attitude and Altitude System of a Quadcopter UAV via Neural Network

et al. 2021

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In this paper, a sliding mode control based on neural networks is proposed for attitude and altitude system of quadcopter under external disturbances. First, the dynamic model of the quadcopter is considered under external disturbances. Sliding mode controllers are then integrated with neural network algorithm to achieve the time-varying sliding surface; their coefficients in sliding surface are adjusted through backpropagation law. The disturbance observer is also combined with sliding mode controllers to estimate and handle the external disturbances. Finally, the Lyapunov theory is applied to validate the stability of suggested control method. The performance of proposed sliding mode control has been evaluated using a numerical simulation. The results show that the attitude and altitude controller based on suggested algorithm has a better tracking performance and disturbance rejection.

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Section: Introductionmentioning

confidence: 99%

Adaptive Sliding Mode Control for Attitude and Altitude System of a Quadcopter UAV via Neural Network

et al. 2021

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“…Fortunately, fixed-time or finite-time control methods can improve the transient tracking performance and provide a faster decay rate, as exhibited in Refs. (25), (26), (33) and (36). Moreover, various fruitful studies have demonstrated that prescribed performance control techniques can effectively guarantee the desired transient and steady tracking behaviour (32,41) .…”

Section: Introductionmentioning

confidence: 99%

Neuroadaptive output-feedback trajectory tracking control for a stratospheric airship with prescribed performance

et al. 2020

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ABSTRACT In this article, we investigate the horizontal trajectory tracking problem for an underactuated stratospheric airship subject to nonvanishing external disturbances and model uncertainties. By transforming the tracking errors into new virtual error variables, we can specify the transient and steady-state tracking performance of the resulting nonlinear system quantitatively, which means that under the proposed control scheme, the tracking errors will converge to prescribed residual sets around the origin before a preselected finite time with decay rates no less than a preassignable value. To address unknown items, minimal learning parameter (MLP) techniques for neural networks (NNs) approximation are employed, which efficaciously relax the computational burden, enhance the robustness against dynamics uncertainties and provide an improved property for disturbances rejection. A finite-time convergent observer (FTCO) is incorporated into the control framework to realise output-feedback control, ensuring that estimation errors are bounded during operation and approach zero within a finite time. Stability analysis proves that all the closed-loop signals are uniformly bounded. The effectiveness and advantages of the proposed control strategy are verified by simulation results.

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“…In order to obtain faster convergence of tracking errors for altitude and position, a nonlinear fast sliding mode controller combined with the traditional supertwisting algorithm is presented in the work [21]. In order to compensate for the negative effects of completely unknown input saturation constraints, the study presented in [22] deals with this problem and proposes a novel adaptive robust controller in the presence of unmodeled nonlinear dynamics, input saturation, and external disturbances. The finite-time convergence of the state variables is achieved by the proposed controller, which introduces a backstepping technique and a novel neural network.…”

Section: Introductionmentioning

confidence: 99%

Optimal New Sliding Mode Controller Combined with Modified Supertwisting Algorithm for a Perturbed Quadrotor UAV

Houm

Abbou

Labbadi

et al. 2020

International Journal of Aerospace Engineering

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This paper deals with the design of a novel modified supertwisting fast nonlinear sliding mode controller (MSTFNSMC) to stabilize a quadrotor system under time-varying disturbances. The suggested control strategy is based on a modified supertwisting controller with a fast nonlinear sliding surface to improve the tracking performance. The paper suggests a simple optimization tool built-in MATLAB/Simulink to tune the proposed controller parameters. Fast convergence of state variables is established by using a nonlinear sliding surface for rotational and translational subsystems. The modified supertwisting controller is developed to suppress the effect of chattering, reject disturbances, and ensure robustness against external disturbance effect. The stability of the proposed controller (MSTFNSMC) is proved using the Lyapunov theory. The performance of the proposed MSTFNSMC approach is compared with the supertwisting sliding mode controller (STSMC) by numerical simulations to verify its effectiveness.

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Adaptive neural network finite time control for quadrotor UAV with unknown input saturation

Cited by 90 publications

References 57 publications

Adaptive Sliding Mode Control for Attitude and Altitude System of a Quadcopter UAV via Neural Network

Adaptive Sliding Mode Control for Attitude and Altitude System of a Quadcopter UAV via Neural Network

Neuroadaptive output-feedback trajectory tracking control for a stratospheric airship with prescribed performance

Optimal New Sliding Mode Controller Combined with Modified Supertwisting Algorithm for a Perturbed Quadrotor UAV

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