1,720,965 research outputs found
Consensus-based Control of Multi-Agent Systems: Distributed Target Tracking and Analysis of Time Delays
Get PDFL'abstract è presente nell'allegato / the abstract is in the attachmen
A Review of Consensus-based Multi-agent UAV Implementations
Get PDFIn this paper, a survey on distributed control applications for multi Unmanned Aerial Vehicles (UAVs) systems is proposed.The focus is on consensus-based control, and both rotary-wing and fixed-wing UAVs are considered. On one side, the latest experimental configurations for the implementation of formation flight are analysed and compared for multirotor UAVs. On the other hand, the control frameworks taking into account the mobility of the fixed-wing UAVs performing target tracking are considered. This approach can be helpful to assess and compare the solutions for practical applications of consensus in UAV swarms
Implementation and Performance Evaluation of a Consensus Protocol for Multi-UAV Formation with Communication Delay
Get PDFConsensus theory represents a relevant strategy for the control of distributed multi-UAV missions, whose main feature is the local inter-agent communication. Besides the physical characteristics of the swarm, a proper simulation environment must take into account such communication properties. In this paper, a formation consensus algorithm is implemented in ROS/Gazebo through the use of docker containers, so that the features of a real network can be included in the simulation. Performance metrics are provided to help researchers to validate the impact of communication delays on the performance of the algorithm
Experimental Validation of Multi-UAV Applications: Formation Flight and Decentralized Target Estimation
No full textThis paper describes the experimental validation of two multi-UAV applications in an indoor environment. In particular, a formation flight task and a decentralized estimation procedure are analyzed for quad-copter platforms. First, the experimental setup is discussed, focusing on the hardware chosen for the navigation, control, and guidance layers of the UAVs. For the first application, a flocking protocol previously designed by the authors is implemented, proving satisfactory performance in terms of inter-agent distance and transient behavior. For the second application, a decentralized Kalman Filter is employed to collaboratively estimate the position of an ArUco marker. The experimental results show a successful information fusion by sharing the UAVs' measurements performed by onboard cameras
Comparison of Multiple Models in Decentralized Target Estimation by a UAV Swarm
Get PDFThe decentralized estimation and tracking of a mobile target performed by a group of
unmanned aerial vehicles (UAVs) is studied in this work. A flocking protocol is used for maintaining
a collision-free formation, while a decentralized extended Kalman filter in the information form is
employed to provide an estimate of the target state. In the prediction step of the filter, we adopt and
compare three different models for the target motion with increasing levels of complexity, namely, a
constant velocity (CV), a constant turn (CT), and a full-state (FS) model. Software-in-the-loop (SITL)
simulations are conducted in ROS/Gazebo to compare the performance of the three models. The
coupling between the formation and estimation tasks is evaluated since the tracking task is affected
by the outcome of the estimation process
Dynamically updated digital twin for prognostics and health management: Application in permanent magnet synchronous motor
Get PDFCurrent research on Digital Twin (DT) based Prognostics and Health Management (PHM) focuses on establishment of DT through integration of real-time data from various sources to facilitate comprehensive product monitoring and health management. However, there still exist gaps in the seamless integration of DT and PHM, as well as in the development of DT multi-field coupling modeling and its dynamic update mechanism. When the product experiences long-period degradation under load spectrum, it is challenging to describe the dynamic evolution of the health status and degradation progression accurately. In addition, DT update algorithms are difficult to be integrated simultaneously by current methods. This paper proposes an innovative dual loop DT based PHM framework, in which the first loop establishes the basic dynamic DT with multi-filed coupling, and the second loop implements the PHM and the abnormal detection to provide the interaction between the dual loops through updating mechanism. The proposed method pays attention to the internal state changes with degradation and interactive mapping with dynamic parameter updating. Furthermore, the Independence Principle for the abnormal detection is proposed to refine the theory of DT. Events at the first loop focus on accurate modeling of multi-field coupling, while the events at the second loop focus on real-time occurrence of anomalies and the product degradation trend. The interaction and collaboration between different loop models are also discussed. Finally, the Permanent Magnet Synchronous Motor (PMSM) is used to verify the proposed method. The results show that the modeling method proposed can accurately track the lifecycle performance changes of the entity and carry out remaining life prediction and health management effectively
Dynamically adaptive cascading updates for hierarchical digital twins
No full textTraditional sensors encounter challenges such as high collection costs, insufficient measurement points, and low data quality in the monitoring and maintenance of modern equipment. These challenges significantly affect the effectiveness and efficiency of monitoring and maintenance processes. Digital twin (DT) technology, as a digital replica of physical entities, is regarded as the 'digital sensor' of physical entities due to its high-precision modeling and dynamic updating capabilities. Compared to traditional sensors, DT models provide substantial improvements in both data volume and quality. However, creating a DT model with high precision and robust dynamic characteristics is notably challenging, particularly when the relationships and state features of the physical entity are complex and variable. To address this issue, a cascading update strategy was introduced. This strategy coordinates complex hierarchical DT update tasks, ensuring model accuracy. Furthermore, a signal characteristic-based dynamic adaptive update algorithm is proposed. This algorithm optimizes the DT updating process and enhances the model's dynamic characteristics. The proposed method is validated using experimental data on plunger pump barrel-port plate oil leakage. The results demonstrate that the method significantly improves the accuracy and updating efficiency of the DT model. It achieves a balance between precision and update time costs, enhancing DTs accuracy and practicality as a 'digital sensor'
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