Formation Control of Rigid Graphs with a Flex Node Addition

This paper examines stability properties of distance-based formation control when the underlying topology consists of a rigid graph and a flex node addition. It is shown that the desired equilibrium set is locally asymptotically stable but there exist undesired equilibria. Specifically, we further consider two cases where the rigid graph is a triangle in 2-D and a tetrahedral in 3-D, and prove that any undesired equilibrium point in these cases is unstable. Thus in these cases, the desired formations are almost globally asymptotically stable.Comment: The full version of this paper with general extensions has been submitted to a journal for publicatio

Support Vector Machine for Regression of Ultimate Strength of Trusses: A Comparative Study

Thanks to the rapid development of computer science, direct analyses have been increasingly used in the design of structures in lieu of member-based design methods using the effective length factor. In a direct analysis, the ultimate strength of a whole structure can be sufficiently estimated, so that the need for member capacity checks is eliminated. However, in complicated structural design problems where many structural analyses are required, the use of direct analyses requires an excessive computation cost. In such cases, Machine Learning (ML) algorithms are used to build metamodels that can predict the structural responses without performing costly structural analysis. In this paper, the support vector machine (SVM) algorithm is employed for the first time to develop a metamodel for predicting the ultimate strength of trusses using direct analysis. Several kernel functions for the SVM model, including linear, sigmoid, polynomial, radial basis function (RBF), are considered. A planar 39-bar nonlinear inelastic steel truss is taken to study the performance of the kernel functions. The results confirm the applicability of the SVM-based metamodel for predicting the ultimate strength of trusses. In particular, the RBF appears to be the best kernel among others. This investigation also provides a deeper understanding of the effect of the parameters on the efficiency of the kernel functions

Distributed Stochastic Model Predictive Control for an Urban Traffic Network

In this paper, we design a stochastic Model Predictive Control (MPC) traffic signal control method for an urban traffic network when the uncertainties in the estimation of the exogenous (in/out)-flows and the turning ratios of downstream traffic flows are taken into account. Assuming that the traffic model parameters are random variables with known expectations and variance, the traffic signal control and coordination problem is formulated as a quadratic program with linear and second-order cone constraints. In order to reduce computational complexity, we suggest a way to decompose the optimization problem corresponding to the whole traffic network into multiple subproblems. By applying Alternating Direction Method of Multipliers (ADMM), the optimal stochastic traffic signal splits are found in distributed manner. The effectiveness of the designed control method is validated via some simulations using VISSIM and MATLAB

Nonparametric estimation of the fragmentation kernel based on a PDE stationary distribution approximation

We consider a stochastic individual-based model in continuous time to describe a size-structured population for cell divisions. This model is motivated by the detection of cellular aging in biology. We address here the problem of nonparametric estimation of the kernel ruling the divisions based on the eigenvalue problem related to the asymptotic behavior in large population. This inverse problem involves a multiplicative deconvolution operator. Using Fourier technics we derive a nonparametric estimator whose consistency is studied. The main difficulty comes from the non-standard equations connecting the Fourier transforms of the kernel and the parameters of the model. A numerical study is carried out and we pay special attention to the derivation of bandwidths by using resampling

Ultra high Performance Fiber Reinforced Concrete Panel Subjected to Severe Blast Loading

Experimental studies play a crucial role in shedding light on the dynamic behaviour of structures under blast loading. However, high costs and complicated technical requirements, particularly for full-scale structures, are still huge disadvantages to conduct such a series of tests. Hence, the finite element method is much needed to provide supplementary information to previous experiments and to enable further parametric studies without testing. This article presents a numerical investigation carried out to understand the behaviour of ultra high performance fiber reinforced concrete (UHPFRC) panels under severe blast loading. The authors designed a subroutine with eight numbers of solution-dependent state variables, 32 mechanical constants, integrated with the Abaqus program to analyze the dynamic behaviour of UHPFRC against multiple blast impacts, using the Johnson-Holmquist 2 damage model incorporating both the damage and residual strength of the material. The subroutine was validated by comparing the simulation results with test results. For the purpose of estimating the structural response of the UHPFRC panel subjected to blast loading, other studying scenarios were considered by varying input parameters, including the thickness of the panel, stand-off distance, and steel reinforcement bar volume. The variations in deflection, strain, and damage of the UHPFRC panel, as well as the steel reinforcement strain, were also evaluated. Through important obtained results, the UHPFRC panel is strongly recommended for a protective barrier installed in the vicinity of critical infrastructure against severe blast loadin