Distributed bearing-based formation maneuver control of fixed-wing UAVs by finite-time orientation estimation

Zhang, Yuwei; Li, Shaoshi; Wang, Shaoping; Wang, Xingjian; Duan, Haibin

doi:10.1016/j.ast.2023.108241

Cited by 39 publications

(14 citation statements)

References 44 publications

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“…Remark 4. Assumptions 3 and 4 are prevalent in the bearing-based formation control problem with a leader-follower structure [37][38][39]. Given that the formation controls designed are unavailable for the unique target formation, Assumption 3 is fundamental for the leader-follower structured UAV problem, as stipulated by Lemma 1.…”

Section: Problem Formulationmentioning

confidence: 99%

Dynamics Event-Triggered-Based Time-Varying Bearing Formation Control for UAVs

Ding,

Zhang,

Zhang

2024

Drones

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This article addresses the leader-follower formation maneuver control problem of multiple unmanned aerial vehicles (UAVs), taking into account the time-varying velocity and time-varying relative bearing. An event-triggered bearing-based distributed velocity observer was designed, using only the desired position and velocity of the leaders. Furthermore, a dynamic event-triggered mechanism was introduced to reduce continuous communication between UAVs, thus effectively saving communication bandwidth and resources. Building on this, a bearing-only formation maneuver control strategy was proposed, integrating the event-triggered velocity observer with the backstepping control approach. To conclude, numerical simulations have been conducted to confirm the effectiveness of the proposed scheme in accomplishing formation maneuver control objectives, including translation, scaling, and rotation control. Furthermore, the advantages of the dynamic event-triggering strategy have been demonstrated through comparative simulations with traditional event-triggering strategies. Additionally, the effectiveness of the proposed observer and controller has been demonstrated by a comprehensive hardware-in-the-loop (HITL) simulation example.

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Section: Problem Formulationmentioning

confidence: 99%

Dynamics Event-Triggered-Based Time-Varying Bearing Formation Control for UAVs

Ding,

Zhang,

Zhang

2024

Drones

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“…Many efforts have been devoted to the finite-time consensus problem of systems with general linear [7], high-order integrator [8,9], other nonlinear dynamics such as typical Euler-Lagrange dynamics [10] and attitude consensus of spacecraft [11][12][13]. Compared with asymptotic control, which requires the system to converge to a given bound exponentially, a finite-time control law can lead to a faster convergence rate (near the equilibrium point) [14,15]. However, the convergent time resulting from finite-time control schemes heavily depends on the initial conditions.…”

Section: Introductionmentioning

confidence: 99%

Fixed-time Consensus-based Attitude Control of Large Telescope On-orbit Construction

Li,

Guo,

Yin

et al. 2024

Preprint

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This study investigates the fixed-time consensus-based attitude control problem for large telescope on-orbit construction subject to discrete-time communication, unknown tiny disturbance torque, model uncertainty, and actuator saturation under an undirected communication graph. Motivated by the need to save wireless communication resources, the communication scheme was designed to exploit interference, which can save communication resources in proportion to the number of modular unit mirrors. Then, a fixed-time observer with discrete-time communication was proposed, which also handles the unknown channel attenuation problem when exploiting information interference. Moreover, based on the backstepping procedure and the suitably defined adaptive compensation law, the fixed-time attitude consensus control law was proposed to handle unknown tiny disturbance torque, model uncertainty, and actuator saturation. Finally, comparative simulation results were provided to verify the effectiveness of the fixed-time observer and proposed fixed-time control law.

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“…Many researchers have attempted to investigate accident-contributing elements; however, little work has been given to explaining black box models [ 27 , 28 ]. The authors applied five machine learning models and explainable machine learning [ 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

Improving traffic accident severity prediction using MobileNet transfer learning model and SHAP XAI technique

Aboulola

2024

PLoS ONE

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Traffic accidents remain a leading cause of fatalities, injuries, and significant disruptions on highways. Comprehending the contributing factors to these occurrences is paramount in enhancing safety on road networks. Recent studies have demonstrated the utility of predictive modeling in gaining insights into the factors that precipitate accidents. However, there has been a dearth of focus on explaining the inner workings of complex machine learning and deep learning models and the manner in which various features influence accident prediction models. As a result, there is a risk that these models may be seen as black boxes, and their findings may not be fully trusted by stakeholders. The main objective of this study is to create predictive models using various transfer learning techniques and to provide insights into the most impactful factors using Shapley values. To predict the severity of injuries in accidents, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Residual Networks (ResNet), EfficientNetB4, InceptionV3, Extreme Inception (Xception), and MobileNet are employed. Among the models, the MobileNet showed the highest results with 98.17% accuracy. Additionally, by understanding how different features affect accident prediction models, researchers can gain a deeper understanding of the factors that contribute to accidents and develop more effective interventions to prevent them.

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Distributed bearing-based formation maneuver control of fixed-wing UAVs by finite-time orientation estimation

Cited by 39 publications

References 44 publications

Dynamics Event-Triggered-Based Time-Varying Bearing Formation Control for UAVs

Dynamics Event-Triggered-Based Time-Varying Bearing Formation Control for UAVs

Fixed-time Consensus-based Attitude Control of Large Telescope On-orbit Construction

Improving traffic accident severity prediction using MobileNet transfer learning model and SHAP XAI technique

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