Coordinated Path Following of UAVs over Time-Varying Digraphs Connected in an Integral Sense

This paper presents a new connectivity condition on the information flow between UAVs to achieve coordinated path following. The information flow is directional, so that the underlying communication network topology is represented by a time-varying digraph. We assume that this digraph is connected in an integral sense. This is a much more general assumption than the one currently used in the literature. Under this assumption, it is shown that a decentralized coordination controller ensures exponential convergence of the coordination error vector to a neighborhood of zero. The efficacy of the algorithm is confirmed with simulation results

Coordinated Path Following of UAVs using Event-Triggered Communication over Time-Varying Networks with Digraph Topologies

In this article, a novel time-coordination algorithm based on event-triggered communications is proposed to achieve coordinated path-following of UAVs. To be specific, in the approach adopted a UAV transmits its progression information over a time-varying network to its neighbors only when a decentralized trigger condition is satisfied, thereby significantly reducing the volume of inter-vehicle communications required when compared with the existing algorithms based on continuous communications. Using such intermittent communications, it is shown that a decentralized coordination controller guarantees exponential convergence of the coordination error to a neighborhood of zero. Also, a lower bound on the interval between two consecutive event-triggered times is provided showing that the chattering issue does not arise with the proposed algorithm. Finally, simulation results validate the efficacy of the proposed algorithm.Comment: arXiv admin note: text overlap with arXiv:2307.0655

Guaranteed Nonlinear Tracking in the Presence of DNN-Learned Dynamics With Contraction Metrics and Disturbance Estimation

This paper presents an approach to trajectory-centric learning control based on contraction metrics and disturbance estimation for nonlinear systems subject to matched uncertainties. The proposed approach allows for the use of deep neural networks to learn uncertain dynamics while still providing guarantees of transient tracking performance throughout the learning phase. Within the proposed approach, a disturbance estimation law is adopted to estimate the pointwise value of the uncertainty, with pre-computable estimation error bounds (EEBs). The learned dynamics, the estimated disturbances, and the EEBs are then incorporated in a robust Riemannian energy condition to compute the control law that guarantees exponential convergence of actual trajectories to desired ones throughout the learning phase, even when the learned model is poor. On the other hand, with improved accuracy, the learned model can be incorporated into a high-level planner to plan better trajectories with improved performance, e.g., lower energy consumption and shorter travel time. The proposed framework is validated on a planar quadrotor navigation example.Comment: Shorter version submitted to CDC 202

Determining Visitor Engagement through Augmented Reality at Science Festivals: An Experience Economy Perspective

Augmented reality (AR) has been increasingly implemented to enhance visitor experiences, and tourism research has long understood the importance of creating memorable experiences, leading to the research era of experience economy. Although technology-enhanced visitor engagement is crucial for science festivals, research focusing on visitor engagement through AR using the experience economy perspective is limited. Therefore, the aim of this study is to examine how the educational, esthetics, escapist and entertainment experience using AR affect visitor satisfaction and memorable experience, and eventually, lead to visitor engagement with science experiences in the context of science festivals. A total of 220 data inputs were collected as part of the European City of Science festivities and Manchester Science Festival 2016 and analyzed using structural equation modelling. Findings show that the four realms of experience economy influence satisfaction and memory and, ultimately, the intention for visitor engagement with science research at science festivals. Theoretical contributions and practical implications are presented and discussed

Influence of Solute Size on Membrane Fouling during Polysaccharide Enrichment Using Dense Polymeric UF Membrane: Measurements and Mechanisms

Fouling mechanisms associated with membrane-based polysaccharide enrichment were determined using a dense ultrafiltration (UF) membrane. Dextran with different molecular weights (MWs) was used as a surrogate for polysaccharides. The influence of dextran MW on fouling mechanisms was quantified using the Hermia model. Flux data obtained with different dextran MWs and filtration cycles were plotted to quantify the more appropriate fouling mechanisms among complete pore blocking, standard pore blocking, intermediate pore blocking, and cake filtration. For 100,000 Da dextran, all four mechanisms contributed to the initial fouling. As the filtration progressed, the dominant fouling mechanism appeared to be cake filtration with a regression coefficient (R2) of approximately 0.9519. For 10,000 Da, the R2 value for cake filtration was about 0.8767 in the initial filtration. Then, the R2 value gradually decreased as the filtration progressed. For 6000 Da, the R2 values of the four mechanisms were very low in the initial filtration. However, as the filtration progressed, the R2 value for cake filtration reached 0.9057. These results clearly show that the fouling mechanism of dense UF membranes during polysaccharide enrichment can be quantified. In addition, it was confirmed that the dominant fouling mechanism can change with the size of the polysaccharide and the duration of filtration