Adaptive coordinated control of engine speed and battery charging voltage

In this paper, the control problem of auxiliary power unit (APU) for hybrid electric vehicles is investigated.An adaptive controller is provided to achieve the coordinated control between the engine speed and the battery charging voltage. The proposed adaptive coordinated control laws for the throttle angle of the engine and the voltage of the powerconverter can guarantee not only the asymptotic tracking performance of the engine speed and the regulation of the battery charging voltage, but also the robust stability of the closed loop system under external load changes. Simulation results are given to verify the performance of the proposed adaptive controller.     

Nonlinear control for robust rejection of periodic disturbances

Consider a linear plant with a strictly proper rational transfer function and an input which is a known periodic waveform of unknown amplitude and phase. In the case when the periodic waveform has infinitely many non-zero harmonics, it is pointed out that for a wide class of linear, time-invariant (infinite-dimensional) controllers, it is not possible to robustly stabilize the plant and achieve asymptotic rejection of the disturbance. For a specific linear plant and a triangular wave disturbance, it is shown by construction that the problem is soluble with a nonlinear controller. Robustness is measured using the gap metric robust stability margin.     

Correlation structure of training data and the fitting ability of back propagation networks: Some experimental results

White [6–8] has theoretically shown that learning procedures used in network training are inherently statistical in nature. This paper takes a small but pioneering experimental step towards learning about this statistical behaviour by showing that the results obtained are completely in line with White's theory. We show that, given two random vectorsX (input) andY (target), which follow a two-dimensional standard normal distribution, and fixed network complexity, the network's fitting ability definitely improves with increasing correlation coefficient r_XY (0r_XY1) betweenX andY. We also provide numerical examples which support that both increasing the network complexity and training for much longer do improve the network's performance. However, as we clearly demonstrate, these improvements are far from dramatic, except in the case r_XY=+ 1. This is mainly due to the existence of a theoretical lower bound to the inherent conditional variance, as we both analytically and numerically show. Finally, the fitting ability of the network for a test set is illustrated with an example.Nomenclature X Generalr-dimensional random vector. In this work it is a one-dimensional normal vector and represents the input vector - Y Generalp-dimensional random vector. In this work it is a one-dimensional normal vector and represents the target vector - Z Generalr+p dimensional random vector. In this work it is a two-dimensional normal vector - E Expectation operator (Lebesgue integral) - g(X)=E(Y¦X) Conditional expectation - Experimental random error (defined by Eq. (2.1)) - y Realized target value - o Output value - f Network's output function. It is formally expressed asf: R ^r×WR ^p, whereW is the appropriate weight space - Average (or expected) performance function. It is defined by Eq. (2.2) as (w)=E[(Yf(X,w)],w W - Network's performance - w ^* Weight vector for optimal solution. That is, the objective of network is such that (w ^*) is minimum - C ₁ Component one - C ₂ Component two - Z Matrix of realised values of the random vectorZ overn observations - Z _t Transformed matrix version ofZ in such a way thatX andY have values in [0,1] - X _t ,Y _t Transformed versions ofX andY and both are standard one-dimensional normal vectors - n _h Number of hidden nodes (neurons) - r _XY Correlation coefficient between eitherX andY orX _t andY _t - _s and _k in Eq. (3.1) and afterwards _s is average value of 100 differentZ _t matrices. _k is the error function ofkthZ _t , matrix. In Eq. (3.1), the summation is fromk=1 to 100, and in Eq. (3.2) fromi=1 ton. In Eq. (3.2)o _ki andy _ki are the output and target values for the kthZ _t matrix and ith observation, respectively - ^1/2(w ^*) and _k (w_n) k(w_n) is the sample analogue of ^1/2(w ^*) - _Y ² In Eq. (4.1) and afterwards, _Y ² is the variance ofY - _Y ² variance ofY _t . In Sect. 4.3 the transformation isY _t=a Y+b - Y _max,Y _min the maximum and minimum values ofY - R Correlation matrix ofX andY - Covariance matrix ofX andY - Membership symbol in set theory     

Asymptotic Preserving scheme for strongly anisotropic parabolic equations for arbitrary anisotropy direction

This paper deals with the numerical study of a strongly anisotropic heat equation. The use of standard schemes in this situation leads to poor results, due to high anisotropy. Furthermore, the recently proposed Asymptotic-Preserving method (Lozinski et al., 2012) allows one to perform simulations regardless of the anisotropy strength but its application is limited to the case where the anisotropy direction is given by a field whose lines are all open. In this paper we introduce a new Asymptotic-Preserving method, which overcomes those limitations without any loss of precision or increase in computational costs. The convergence of the method is shown to be independent of the anisotropy parameter 0<ε<1$0<\epsilon <1$ for fixed coarse Cartesian grids, and for variable anisotropy directions. The context of this work is magnetically confined fusion plasmas.     

A multi-agent approach to intelligent monitoring in smart grids

In this paper, we propose a scalable multi-agent architecture to give support to smart grids, paying special attention to the intelligent monitoring of distribution substations. The data gathered by multiple sensors are used by software agents that are responsible for monitoring different aspects or events of interest, such as normal voltage values or unbalanced intensity values that can end up blowing fuses and decreasing the quality of service of end consumers. The knowledge bases of these agents have been built by means of a formal model for normality analysis that has been successfully used in other surveillance domains. The architecture facilitates the integration of new agents and can be easily configured and deployed to monitor different environments. The experiments have been conducted over a power distribution network.     

A NOTE ON ASYMPTOTIC INFERENCE IN AUTOREGRESSIVE MODELS WITH ROOTS ON THE UNIT CIRCLE

Abstract. An estimation and inference procedure is proposed for parameters of the p th order autoregressive model with roots both on the unit circle and outside the unit circle. The procedure is motivated by the fact that the parameter estimates of the nonstationary part of the model have higher order consistency properties than the parameter estimates of the stationary part. The procedure allows the use of the known asymptotic distributional results of purely nonstationary models and purely stationary models. Only ordinary least squares routines are needed.     

Smooth surfaces from rational bilinear patches

Smooth freeform skins from simple panels constitute a challenging topic arising in contemporary architecture. We contribute to this problem area by showing how to approximate a negatively curved surface by smoothly joined rational bilinear patches. The approximation problem is solved with help of a new computational approach to the hyperbolic nets of Huhnen-Venedey and Rörig and optimization algorithms based on it. We also discuss its limits which lie in the topology of the input surface. Finally, freeform deformations based on Darboux transformations are used to generate smooth surfaces from smoothly joined Darboux cyclide patches; in this way we eliminate the restriction to surfaces with negative Gaussian curvature.     

删失数据下部分线性模型的估计及渐近正态性

研究了删失数据下的部分线性回归模型,通过数据变换,利用局部多项式方法和两阶段估计,给出了函数系数和参数部分的估计。并给出了估计的渐近正态性。     

水文时间序列变点分析的可靠性检验

针对不符合正态分布的水文时间序列,建立用于时间序列变点分析的贝叶斯数学模型.研究了某水文站1942—2008年共67年年径流最大值时间序列的突变,得到1989年是最大可能变异点.贝叶斯数学模型中仅给出了变点位置的后验概率,但是最大后验概率时间位置处是否确实发生变异,则有待进一步讨论.因此文中认为需要进一步检验1989年前后的变化是否在随机波动范围内或者是确实发生了变异.文中采用再抽样自助法对结果进行深入的可靠性分析,证实1989年前后确实发生了突变,变异程度远远超过了随机波动范围.考虑到时间序列的随机性,建议在今后工作中采用贝叶斯变点分析模型确定变异点时,采用自助再抽样方法对结果进行可靠性分析.