Observer-Based Adaptive Fuzzy Finite-Time Attitude Control for Quadrotor UAVs

Liu, Kang; Yang, Po; Wang, Rujing; Jiao, Lin; Li, Tao; Zhang, Jie

doi:10.1109/taes.2023.3308552

Cited by 39 publications

(30 citation statements)

References 51 publications

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“…The main contributions of this paper are summarized as 1. Compared with the finite-time scheme [27,28,30], a DOB with fixed-time convergence characteristics is proposed, which is used to estimate external wind disturbances for feedforward compensation. In addition, the convergence time of the estimation error is bounded and independent of the initial state of the system.…”

Section: Introductionmentioning

confidence: 99%

Fixed-time trajectory tracking control of a quadrotor UAV under time-varying wind disturbances: theory and experimental validation

Cai,

Zhu,

Yao

2024

Meas. Sci. Technol.

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This paper adopts a fixed-time method to study the trajectory tracking issue of a quadrotor unmanned aerial vehicle (UAV) under time-varying wind disturbances. Firstly, in order to address the impact of time-varying wind disturbances on UAV, a fixed-time disturbance observer (FxTDO) is constructed to accurately estimate wind disturbances. Secondly, to improve control accuracy and convergence rate, the robust fixed-time controller is designed for the position and attitude system by combining the sliding mode control theory with fixed-time technique. Furthermore, it is rigorously analyzed that the tracking error of the observer and controller can converge to zero via Lyapunov criterion, and the convergence time is independent of the initial state.Finally, the effectiveness and robustness of the designed control strategy are verified by numerical simulations and actual flight experiments, providing an effective connection between control theory and practical applications.

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Section: Introductionmentioning

confidence: 99%

Fixed-time trajectory tracking control of a quadrotor UAV under time-varying wind disturbances: theory and experimental validation

Cai,

Zhu,

Yao

2024

Meas. Sci. Technol.

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“…There are two main methods for bearing fault diagnosis: signal-based processing and a data-driven approach. Data-driven methods have made significant strides in fault diagnosis due to their powerful nonlinear self-learning [2,3] and intelligent fault diagnosis capabilities [4][5][6][7][8][9][10][11][12][13][14][15][16]. For example, convolutional neural networks (CNNs) [4][5][6][7], generative adversarial networks (GANs) [8][9][10][11], long short-term memory networks [12][13][14], and deep residual shrinkage networks (DRSNs) [15,16] have been widely adopted.…”

Section: Introductionmentioning

confidence: 99%

A Bearing Fault Diagnosis Method under Small Sample Conditions Based on the Fractional Order Siamese Deep Residual Shrinkage Network

Li,

Wu,

Luo

et al. 2024

Fractal Fract

Self Cite

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A bearing fault is one of the major causes of rotating machinery faults. However, in real industrial scenarios, the harsh and complex environment makes it very difficult to collect sufficient fault data. Due to this limitation, most of the current methods cannot accurately identify the fault type in cases with limited data, so timely maintenance cannot be conducted. In order to solve this problem, a bearing fault diagnosis method based on the fractional order Siamese deep residual shrinkage network (FO-SDRSN) is proposed in this paper. After data collection, all kinds of vibration data are first converted into two-dimensional time series feature maps, and these feature maps are divided into the same or different types of fault sample pairs. Then, a Siamese network based on the deep residual shrinkage network (DRSN) is used to extract the features of the fault sample pairs, and the fault type is determined according to the features. After that, the contrastive loss function and diagnostic loss function of the sample pairs are combined, and the network parameters are continuously optimized using the fractional order momentum gradient descent method to reduce the loss function. This improves the accuracy of fault diagnosis with a small sample training dataset. Finally, four small sample datasets are used to verify the effectiveness of the proposed method. The results show that the FO-SDRSN method is superior to other advanced methods in terms of training accuracy and stability under small sample conditions.

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“…A reduced-order model of the system was used to build the adaptive observer design, which lowered computational complexity and made parameter adjustments easier. In [20], without accurate knowledge of the system dynamics, an adaptive neural network observer was designed to estimate information on angular velocity and compensate for the disturbance of the four-rotor UAV to guarantee the stability of the system. Since the system state measurement and state-dependent disturbances cannot be fed back, an adaptive approach based on a fuzzy observer was provided in [21].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Nonsingular Fast-Reaching Terminal Sliding Mode Control Based on Observer for Aerial Robots

Yang,

Xuan,

2024

Actuators

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In this article, an observer-based adaptive non-singular fast-reaching terminal sliding mode control strategy is proposed to tackle the problem of actuator faults and uncertain disturbance in aerial robot systems. Firstly, a model of an aerial robot system is established through dynamic analysis. Next, an adaptive observer, combined with a fast adaptive fault estimation (FAFE) algorithm, is proposed to estimate system states and actuator failure and compensate for faults in a precise and prompt manner. In addition, a non-singular fast terminal sliding surface is defined, taking into account the fast convergence of the tracking errors in order to provide appropriate trajectory tracking results. Since the upper bounds of the disturbances caused by the manipulator of the system in practice are unknown, the control approach may benefit from the addition of an adaptive control strategy that can suppress the influence of uncertain disturbances. The Lyapunov stability theory demonstrates that tracking errors are able to converge stably and quickly. In the end, the contrast experiment is conducted to exhibit the effectiveness of the proposed control strategy. The results demonstrate quicker convergence and improved estimating accuracy.

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Observer-Based Adaptive Fuzzy Finite-Time Attitude Control for Quadrotor UAVs

Abstract: Observer-based adaptive fuzzy finite-time attitude control for quadrotor UAVs. IEEE Transactions on Aerospace and Electronic Systems.

Cited by 39 publications

References 51 publications

Fixed-time trajectory tracking control of a quadrotor UAV under time-varying wind disturbances: theory and experimental validation

Fixed-time trajectory tracking control of a quadrotor UAV under time-varying wind disturbances: theory and experimental validation

A Bearing Fault Diagnosis Method under Small Sample Conditions Based on the Fractional Order Siamese Deep Residual Shrinkage Network

Adaptive Nonsingular Fast-Reaching Terminal Sliding Mode Control Based on Observer for Aerial Robots

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