This paper addresses the problem of formation control for a quadrotor swarm (QS) system with directed graph topology under external environmental disturbances and unreliable internal state acquisition. The proposed di...
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This paper addresses the problem of formation control for a quadrotor swarm (QS) system with directed graph topology under external environmental disturbances and unreliable internal state acquisition. The proposed distributed robust control framework, based on a gemetric controller, incorporates ${\mathcal {L}}_control$ adaptive controllers and differentiator systems. First, the geometric formation controller is designed to implement the formation control of the nominal system. Then, ${\mathcal {L}}_control$ adaptive controllers are designed separately for each quadrotor’s position loop and attitude loop subsystems to address the effects of uncertainties such as external time-varying disturbances (matched and unmatched disturbances) and different mass variations of quadrotors. Furthermore, the differentiator system is devised to accurately estimate the higher-order derivatives of the non-directly-measurable velocity information and the virtual translation control signal, which enhances system accuracy while reducing computational complexity. The Lyapunov stability theory is employed to analyze the stability of the closed-loop system. Finally, the effectiveness and exceptional performance of this approach in QS formation control were validated through numerical simulation and experimental results. Note to Practitioners—The inspiration for this article comes from the issue of formation control in a cluster of quadrotor drones, which is also applicable to formation control in other types of drones. In this paper, a formation control algorithm based on ${\mathcal {L}}_control$ adaptive control strategy and arbitrary-order differentiation is designed. This algorithm can address not only the issue of time-varying wind disturbances frequently encountered during quadrotor drone flights but also the effects of unpredictable velocities and inconsistent masses of quadrotor drones. The disturbance rejection capability of this scheme enables quadrotor drones to be applied more safely and r
In this paper, we study the fault detection along with the estimation problem for an unstable wave equation based on an adaptive extended state observer (ESO) using only boundary measurements. A fault detection filter...
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Affordance refers to the functional properties that an agent perceives and utilizes from its environment, and is key perceptual information required for robots to perform actions. This information is rich and multimod...
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The quantity forecast of incoming dustcarts in the waste transfer station is essential to enhancing the operational efficiency of smart sanitation, because it is helpful for the station management and the planning of ...
The quantity forecast of incoming dustcarts in the waste transfer station is essential to enhancing the operational efficiency of smart sanitation, because it is helpful for the station management and the planning of resources. In this study, a seasonal autoregressive integrated moving average (SARIMA) model is suggested to forecast the incoming dustcarts of a waste transfer station. The dataset utilized contains both the hourly-sampled quantity and proportion of residual waste dustcarts. The outcomes of single step and multi-step forecasting are examined with different performance measures in order to confirm SARIMA’s effectiveness. The experimental results show that the SARIMA model has better prediction results compared with the LSTM model. In addition, SARIMA model has high accuracy in both single step and multi-step forecasting, but multi-step forecasting is more effective for solving real-world issues because of its less time-consuming.
Deception attacks are employed to compromise cyber-physical systems through fake data injection. This paper concentrates on the distributed resilient estimation issue of multi-sensor networked systems under deception ...
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ISBN:
(数字)9798331518493
ISBN:
(纸本)9798331518509
Deception attacks are employed to compromise cyber-physical systems through fake data injection. This paper concentrates on the distributed resilient estimation issue of multi-sensor networked systems under deception attacks. In order to detect deception attacks, we utilize Kullback-Leibler(K-L) divergence as a criterion to distinguish the discrepancy between the deceived information and the estimated information. When the attack does not exist, the transmitted information can be restored to ensure the resilient estimation performance. Based on the extended Kalman filter design method, a distributed resilient estimation with a dual-gain mechanism is developed. This advanced approach dynamically adjusts the weighting balance between the predictive model and sensor data inputs, achieving the optimal estimation during the shutdown and activation of spoofing attacks. Finally, numerical simulations are provided to further illustrate the results.
This study proposes an image-based visual servoing(IBVS)method based on a velocity observer for an unmanned aerial vehicle(UAV)for tracking a dynamic target in Global Positioning System(GPS)-denied *** proposed method...
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This study proposes an image-based visual servoing(IBVS)method based on a velocity observer for an unmanned aerial vehicle(UAV)for tracking a dynamic target in Global Positioning System(GPS)-denied *** proposed method derives the simplified and decoupled image dynamics of underactuated UAVs using a constructed virtual camera and then considers the uncertainties caused by the unpredictable rotations and velocities of the dynamic target.A novel image depth model that extends the IBVS method to track a rotating target with arbitrary orientations is *** depth model ensures image feature accuracy and image trajectory smoothness in rotating target *** relative velocities of the UAV and the dynamic target are estimated using the proposed velocity *** to the velocity observer,translational velocity measurements are not required,and the control chatter caused by noise-containing measurements is *** integral-based filter is proposed to compensate for unpredictable environmental disturbances in order to improve the antidisturbance *** stability of the velocity observer and IBVS controller is analyzed using the Lyapunov *** simulations and multistage experiments are conducted to illustrate the tracking stability,anti-disturbance ability,and tracking robustness of the proposed method with a dynamic rotating target.
Continuous-variable quantum key distribution (CVQKD) is a mature technology that can theoretically provide an unconditional security guarantee. However, a practical CVQKD system may be vulnerable to various quantum ha...
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Continuous-variable quantum key distribution (CVQKD) is a mature technology that can theoretically provide an unconditional security guarantee. However, a practical CVQKD system may be vulnerable to various quantum hacking attacks due to imperfect devices and insufficient assumptions. In this paper, we propose a universal defense strategy called a machine-learning-based attack detection scheme (MADS). Leveraging the combined advantages of density-based spatial clustering of applications with noise (DBSCAN) and multiclass support vector machines (MCSVMs), MADS demonstrates remarkable effectiveness in detecting quantum hacking attacks. Specifically, we first establish a set of attack-related features to extract feature vectors. These vectors are then utilized as input data for DBSCAN to identify and remove any noise or outliers. Finally, the trained MCSVMs are employed to classify and predict the processed data. The predicted results can immediately determine whether or not to generate a final secret key. Simulation results show that the proposed MADS can efficiently detect most quantum hacking attacks and revise the overestimated secret key rates caused by a CVQKD system without any defense strategy to obtain a tighter security bound.
visual Inertial Odometry(VIO) is widely used in various fields. When lighting conditions change dramatically, the visual front-end is affected, resulting in performance degradation and even failure in some extreme sce...
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ISBN:
(纸本)9781665481106
visual Inertial Odometry(VIO) is widely used in various fields. When lighting conditions change dramatically, the visual front-end is affected, resulting in performance degradation and even failure in some extreme scenarios. In this paper, a stereo VIO based on an equivariant filter(EqF) for complex lighting environments is proposed. We propose a new adaptive gamma correction method, which can effectively improve the quality of image sequence and ensure the real-time performance of the algorithm because of the image selection processing strategy. Our VIO uses a robust stereo visual front-end and a state-of-the-art equivariant filter at the back-end. Experiments showed that the system has higher accuracy and robustness than another EqF-based VIO algorithm(EqF_VIO), and can work in complex and extreme lighting environments.
Surgical navigation based on multimodal image registration has played a significant role in providing intraoperative guidance to surgeons by showing the relative position of the target area to critical anatomical stru...
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Homography estimation is a crucial problem in computer vision, which aims to provide an optimal transformation matrix for aligning images captured from different viewpoints. Current methods extract shallow features fr...
Homography estimation is a crucial problem in computer vision, which aims to provide an optimal transformation matrix for aligning images captured from different viewpoints. Current methods extract shallow features from image pairs and introduce learnable mask modules to improve homography estimation performance. However, they struggle to capture long-term dependencies between features and comprehend the global structures of image features. A deep unsupervised homography learning framework is proposed in this paper, consisting of a weight-sharing feature extraction network and a homography estimation network based on the Transformer model. The former extracts the local features of images, while the latter learns the correlation between them and understands the global features of images, enabling the algorithm to better estimate the homography of unaligned images. Experimental results demonstrate that the proposed method outperforms the advanced methods for estimating homography matrices in the CA-Unsupervised dataset.
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