Robust Backstepping Control Combined with Fractional-Order Tracking Differentiator and Fractional-Order Nonlinear Disturbance Observer for Unknown Quadrotor UAV Systems

Park, Sung Bum; Han, Seong-Ik

doi:10.3390/app122211637

Cited by 5 publications

(3 citation statements)

References 55 publications

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“…In view of the characteristics of UAV such as underactuation and strong coupling, PID control [2], sliding mode control [3], backstepping control [4] and neural network control [5] have been widely used in UAV control systems. In [6], fuzzy cascade PID and conventional cascade PID autonomously switching control strategy is used to improve the dynamic performance of UAV control systems, and the effectiveness of the algorithm is verified by actual flight.…”

Section: Introductionmentioning

confidence: 99%

Finite-time control based on RBF neural network for quadrotor UAVs with varied mass load

Duan,

Zhou,

Zhao

et al. 2023

J. Phys.: Conf. Ser.

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Aiming at the problem of the gradual reduction of the weight and the external wind disturbance affect flight performance of the quadrotor Unmanned Aerial Vehicle (UAV), a dual-loop finite time control strategy based on Radial Basis Function (RBF) neural network is proposed. The UAV model under disturbance is decoupled into position outer loop subsystem and attitude inner loop subsystem. In the outer loop, the changing weight and the external wind disturbance are approximated by using RBF neural network, command filter is used to avoid the “computing explosion” problem in the traditional backstepping method, and the finite-time control method is able to improve the convergence speed of the position. In the inner loop, the cascade RBF neural network PID control which relies on the self-learning of neural network to realize the dynamic tuning of PID parameters is adopted to achieve rapid convergence of the attitude angle. The simulation results show that compared with the traditional backstepping method and cascade PID control, the convergence time is reduced by 31% on average, which verifies the superiority and effectiveness of the proposed control strategy.

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Section: Introductionmentioning

confidence: 99%

Finite-time control based on RBF neural network for quadrotor UAVs with varied mass load

Duan,

Zhou,

Zhao

et al. 2023

J. Phys.: Conf. Ser.

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“…In [14], the fractional order adaptive controller is designed by FOTD to obtain the differential signal, and the stability of the adaptive system is analyzed. In [15], a fractional order nonlinear disturbance observer based on FOTD is designed to improve the control performance of UAV in the disturbance environment.…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation of LFM Signals Based on FOTD-CFRFT under Impulsive Noise

Wang,

Guo,

Yang

2023

Fractal Fract

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Due to the short duration and high amplitude characteristics of impulsive noise, these parameter estimation methods based on Gaussian assumptions are ineffective in the presence of impulsive noise. To address this issue, a LFM signal parameter estimation method is proposed based on FOTD and CFRFT. Firstly, the mathematical expression of FOTD is presented and its tracking performance is verified. Secondly, the tracked signal is subjected to discrete time CFRFT, and a mathematical optimization model for LFM signal parameter estimation is established on the fractional spectrum characteristic. Finally, a correction method for non-standard SαS distributed noise is proposed, and the performance of parameter estimation under both standard and non-standard SαS distributions are analyzed. The simulation results show that this method not only effectively suppresses the impact of impulsive noise on the fractional spectrum of LFM signal, but also has better parameter estimation accuracy and stability in the low GSNR. The proposed method is particularly effective under the measured noise environment, as it successfully suppresses the impact of impulsive noise and achieves high-precision parameter estimation.

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“…In addition, the UAV in practical applications is affected by internal uncertainties and external disturbances. Several methods have been used to solve external disturbances, such as disturbance observer [23][24][25], expansion state observer [26] and adaptive control [27,28]. Among these anti-disturbance methods, adaptive control can effectively approximate and compensate unknown bounded disturbances and it is widely used in nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Backstepping Control of Quadrotor UAVs with Output Constraints and Input Saturation

Wan

et al. 2023

Applied Sciences

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The control performance of quadrotor unmanned aerial vehicles (UAVs) in complex environments can be affected by external disturbances and other factors. In this paper, an adaptive neural network backstepping controller based on the barrier Lyapunov function (BLF) is designed for a quadrotor UAV with internal uncertainties, input–output constraints and external disturbances. Radial basis function neural networks are used to approximate the uncertainties in the dynamic model of the UAV, while the minimum parameter learning method is combined to accelerate the adjustment speed of neural network weights. A robust term is designed to balance the total system disturbance and improve the anti-interference performance. The BLF is used to handle the output constraint so that the constrained parameters cannot break the predefined constraints. An auxiliary system is introduced to solve input saturation and avoid the dependence of tracking error on the input amplitude in the method of approximating input saturation using the smoothing function. The stability of the control system is demonstrated by the Lyapunov method. The simulation results show that the proposed method has high tracking accuracy compared with the backstepping dynamic surface control method, and the input and output are in the predefined range.

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Robust Backstepping Control Combined with Fractional-Order Tracking Differentiator and Fractional-Order Nonlinear Disturbance Observer for Unknown Quadrotor UAV Systems

Cited by 5 publications

References 55 publications

Finite-time control based on RBF neural network for quadrotor UAVs with varied mass load

Finite-time control based on RBF neural network for quadrotor UAVs with varied mass load

Parameter Estimation of LFM Signals Based on FOTD-CFRFT under Impulsive Noise

Adaptive Backstepping Control of Quadrotor UAVs with Output Constraints and Input Saturation

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