Antisaturation fixed-time attitude tracking control based low-computation learning for uncertain quadrotor UAVs with external disturbances

Liu, Kang; Yang, Po; Jiao, Lin; Wang, Rujing; Yuan, Zhipeng; Dong, Shifeng

doi:10.1016/j.ast.2023.108668

Cited by 22 publications

(6 citation statements)

References 58 publications

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“…where ... r and r (4) are called jerk and snap, respectively. Equation (20) directly relates τ x and τ y to snap by substituting for ωx and ωy from (7).…”

Section: Roll and Pitch Momentsmentioning

confidence: 99%

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Multi-Vehicle Navigation Using Cooperative Localization

Oliveros,

Ashrafiuon

2023

Electronics

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This paper assesses the effectiveness of cooperative localization for improving the performance of closed-loop control systems for networks for autonomous multi-vehicle navigation. Nonlinear dynamic models of two- and three-dimensional vehicles are presented along with their linearized forms. A nonlinear control algorithm is then presented based on the dynamic model. Relative position measurement equations and their linearized forms are introduced. The state and measurement equations are then employed for the propagation and update steps of an EKF-based cooperative localization algorithm. Initially, a series of experiments with networks of quadcopters and mobile robots are presented to validate the performance of cooperative localization for state estimation with the continuous or intermittent presence of absolute measurements or their complete absence. Finally, the performance of the control algorithm is evaluated with and without cooperative localization to demonstrate its effectiveness for improving performance.

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“…where ... r and r (4) are called jerk and snap, respectively. Equation (20) directly relates τ x and τ y to snap by substituting for ωx and ωy from (7).…”

Section: Roll and Pitch Momentsmentioning

confidence: 99%

“…Moreover, CL can aid in reducing the sample rate requirement for SLAM methods in structured environments since these methods require extensive computation. There are also innovative control strategies [3,4] that showcase potential to enhance cooperative localization in multi-agent vehicle networks.…”

Section: Introduction 1motivationmentioning

confidence: 99%

Multi-Vehicle Navigation Using Cooperative Localization

Oliveros,

Ashrafiuon

2023

Electronics

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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

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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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“…Ref. [28] presents a low-computation learning-based antisaturation fixed-time attitudetracking control method. This approach constructs a fixed-time state observer to accurately estimate the system state.…”

Section: Introductionmentioning

confidence: 99%

Fault-Tolerant Event-Triggrred Control for Multiple UAVs with Predefined Tracking Performance

Ma,

Gong,

Wang

2024

Drones

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This paper proposes an event-triggered fault-tolerant time-varying formation control method dedicated to multiple unmanned aerial vehicles (UAVs). We meticulously design a formation-tracking controller with a predefined tracking performance to accommodate the presence of actuator faults and external disturbances. Firstly, the formation-tracking controller acquires the desired heading using the line-of-sight algorithm. Secondly, in the presence of actuator faults and external disturbances, we introduce the radial basis function neural network (RBFNN) and adaptive law tracking control to effectively compensate for their effects. Additionally, we design adaptive tracking controllers and event-triggering conditions to increase the computational frequency. The predefined tracking performance, implemented via a Lyapunov function, ensures the convergence of the tracking error over time. Finally, we conduct a thorough analysis of the system’s stability, successfully eliminating the possibility of Zeno behavior. The simulation results thoroughly validate the effectiveness of the theoretical analysis.

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Antisaturation fixed-time attitude tracking control based low-computation learning for uncertain quadrotor UAVs with external disturbances

Cited by 22 publications

References 58 publications

Multi-Vehicle Navigation Using Cooperative Localization

Multi-Vehicle Navigation Using Cooperative Localization

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

Fault-Tolerant Event-Triggrred Control for Multiple UAVs with Predefined Tracking Performance

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