Distributed sampled-data control of nonholonomic multi-robot systems with proximity networks

This paper considers the distributed sampled-data control problem of a group of mobile robots connected via distance-induced proximity networks. A dwell time is assumed in order to avoid chattering in the neighbor relations that may be caused by abrupt changes of positions when updating information from neighbors. Distributed sampled-data control laws are designed based on nearest neighbour rules, which in conjunction with continuous-time dynamics results in hybrid closed-loop systems. For uniformly and independently initial states, a sufficient condition is provided to guarantee synchronization for the system without leaders. In order to steer all robots to move with the desired orientation and speed, we then introduce a number of leaders into the system, and quantitatively establish the proportion of leaders needed to track either constant or time-varying signals. All these conditions depend only on the neighborhood radius, the maximum initial moving speed and the dwell time, without assuming a prior properties of the neighbor graphs as are used in most of the existing literature.Comment: 15 pages, 3 figure

Analytical coupled-wave model for photonic crystal quantum cascade lasers

A coupled-wave model is developed for photonic-crystal quantum cascade lasers. The analytical model provides an efficient analysis of full three-dimensional large-area device structure, and the validity is confirmed via simulations and previous experimental results.Comment: 21 pages and 8 figure

Flame Extinction and Air Vitiation Effects In FDS In Poorly Ventilated Compartment Fires

Compartment fires with different ventilation conditions exhibit different dynamical behaviors, ranging from steady fuel-limited fires to unsteady air-limited fires. Numerical simulations are here performed to study compartment fires in a configuration corresponding to a scaled-down model developed at University of Maryland, in which experimental data are available. The simulations use Fire Dynamics Simulator (FDS) developed by National Institute of Science and Technology (NIST). Four different cases are studied that are representative of different fire conditions: steady over-ventilated fires; steady under-ventilated fires; and unsteady fires with partial flame quenching; unsteady fires leading to total flame quenching. To account for air vitiation and flame extinction effects, a new flame extinction model is developed and integrated into FDS. It is found that the new model improves the numerical predictions and offers the potential of a better representation of the flame dynamics and upper-layer gas composition

Numerical Simulation of Low-Pressure Explosive Combustion in Compartment Fires

A filtered progress variable approach is adopted for large eddy simulations (LES) of turbulent deflagrations. The deflagration model is coupled with a non-premixed combustion model, either an equilibrium-chemistry, mixture-fraction based model, or an eddy dissipation model. The coupling interface uses a LES-resolved flame index formulation and provides partially-premixed combustion (PPC) modeling capability. The PPC sub-model is implemented into the Fire Dynamic Simulator (FDS) developed by the National Institute of Standards and Technology, which is then applied to the study of explosive combustion in confined fuel vapor clouds. Current limitations of the PPC model are identified first in two separate series of simulations: 1) a series of simulation corresponding to laminar flame propagation across homogeneous mixtures in open or closed tunnel-like configurations; and 2) a grid refinement study corresponding to laminar flame propagation across a vertically-stratified layer. An experimental database previously developed by FM Global Research, featuring controlled ignition followed by explosive combustion in an enclosure filled with vertically-stratified mixtures of propane in air, is used as a test configuration for model validation. Sealed and vented configurations are both considered, with and without obstacles in the chamber. These pressurized combustion cases present a particular challenge to the bulk pressure algorithm in FDS, which has robustness, accuracy and stability issues, in particular in vented configurations. Two modified bulk pressure models are proposed and evaluated by comparison between measured and simulated pressure data in the Factory Mutual Global (FMG) test configuration. The first model is based on a modified bulk pressure algorithm and uses a simplified expression for pressure valid in a vented compartment under quasi-steady conditions. The second model is based on solving an ordinary differential equation for bulk pressure (including a relaxation term proposed to stabilize possible Helmholtz oscillations) and modified vent flow velocity boundary conditions that are made bulk-pressure-sensitive. Comparisons with experiments are encouraging and demonstrate the potential of the new modeling capability for simulations of low pressure explosions in stratified fuel vapor clouds

Steady state behavior of the free recall dynamics of working memory

This paper studies a dynamical system that models the free recall dynamics of working memory. This model is a modular neural network with n modules, named hypercolumns, and each module consists of m minicolumns. Under mild conditions on the connection weights between minicolumns, we investigate the long-term evolution behavior of the model, namely the existence and stability of equilibriums and limit cycles. We also give a critical value in which Hopf bifurcation happens. Finally, we give a sufficient condition under which this model has a globally asymptotically stable equilibrium with synchronized minicolumn states in each hypercolumn, which implies that in this case recalling is impossible. Numerical simulations are provided to illustrate our theoretical results. A numerical example we give suggests that patterns can be stored in not only equilibriums and limit cycles, but also strange attractors (or chaos)

1988 DWC Membership and Mailing Lists

Membership list of DWC members, 198

Controllability of networked multiagent systems based on linearized Turing's model

Turing's model has been widely used to explain how simple, uniform structures can give rise to complex, patterned structures during the development of organisms. However, it is very hard to establish rigorous theoretical results for the dynamic evolution behavior of Turing's model since it is described by nonlinear partial differential equations. We focus on controllability of Turing's model by linearization and spatial discretization. This linearized model is a networked system whose agents are second order linear systems and these agents interact with each other by Laplacian dynamics on a graph. A control signal can be added to agents of choice. Under mild conditions on the parameters of the linearized Turing's model, we prove the equivalence between controllability of the linearized Turing's model and controllability of a Laplace dynamic system with agents of first order dynamics. When the graph is a grid graph or a cylinder grid graph, we then give precisely the minimal number of control nodes and a corresponding control node set such that the Laplace dynamic systems on these graphs with agents of first order dynamics are controllable.Comment: 13 pages, 4 figures, submitted to automatic

A Plug-and-Play Image Registration Network

Deformable image registration (DIR) is an active research topic in biomedical imaging. There is a growing interest in developing DIR methods based on deep learning (DL). A traditional DL approach to DIR is based on training a convolutional neural network (CNN) to estimate the registration field between two input images. While conceptually simple, this approach comes with a limitation that it exclusively relies on a pre-trained CNN without explicitly enforcing fidelity between the registered image and the reference. We present plug-and-play image registration network (PIRATE) as a new DIR method that addresses this issue by integrating an explicit data-fidelity penalty and a CNN prior. PIRATE pre-trains a CNN denoiser on the registration field and "plugs" it into an iterative method as a regularizer. We additionally present PIRATE+ that fine-tunes the CNN prior in PIRATE using deep equilibrium models (DEQ). PIRATE+ interprets the fixed-point iteration of PIRATE as a network with effectively infinite layers and then trains the resulting network end-to-end, enabling it to learn more task-specific information and boosting its performance. Our numerical results on OASIS and CANDI datasets show that our methods achieve state-of-the-art performance on DIR