Joint constraint on the jet structure from the short GRB population and GRB 170817A

The nearest GRB 170817A provided an opportunity to probe the angular structure of the jet of this short gamma-ray burst (SGRB), by using its off-axis observed afterglow emission. It is investigated that whether the afterglow-constrained jet structures can be consistent with the luminosity of the prompt emission of GRB 170817A. Furthermore, by assuming that all SGRBs including GRB 170817A have the same explosive mechanism and jet structure, we apply the different jet structures into the calculation of the flux and redshfit distributions of the SGRB population, in comparison with the observational distributions of the Swift and Fermi sources. As a result, it is found that the single-Gaussian structure can be basically ruled out, whereas the power-law and two-Gaussian models can in principle survive.Comment: 9 pages,6 figure

Nonlinear modelling and adaptive fuzzy control of PEMFC

To improve the stability and performance of fuel cells, the operating temperature of proton exchange membrane fuel cells (PEMFC) should be controlled within a specified range. However, most existing mathematical models of PEMFC are too complex to be applied effectively in the control process. In this paper, adaptive fuzzy identification and control models of PEMFC are developed based on input-output sampled data and experts\u27 experience. The parameters of the identifier and controller are regulated by an adaptive learning algorithm, the network structure and the rule database are modified by adjusting the parameters. In the end, the simulation results of the online control model are presented and show the effectiveness

Identification of discrete-time output error model for industrial processes with time delay subject to load disturbance

In this paper, a bias-eliminated output error model identification method is proposed for industrial processes with time delay subject to unknown load disturbance with deterministic dynamics. By viewing the output response arising from such load disturbance as a dynamic parameter for estimation, a recursive least-squares identification algorithm is developed in the discrete-time domain to estimate the linear model parameters together with the load disturbance response, while the integer delay parameter is derived by using a one-dimensional searching approach to minimize the output fitting error. An auxiliary model is constructed to realize consistent estimation of the model parameters against stochastic noise. Moreover, dual adaptive forgetting factors are introduced with tuning guidelines to improve the convergence rates of estimating the model parameters and the load disturbance response, respectively. The convergence of model parameter estimation is analyzed with a rigorous proof. Illustrative examples for open- and closed-loop identification are shown to demonstrate the effectiveness and merit of the proposed identification method