In this paper, authors consider the existence, uniqueness and nonexistence of the radial ground state to the following p-laplacian equation: Delta(p)u + u(q) - \Du\(sigma) = 0, x epsilon Rn, where 2 less than or equal...In this paper, authors consider the existence, uniqueness and nonexistence of the radial ground state to the following p-laplacian equation: Delta(p)u + u(q) - \Du\(sigma) = 0, x epsilon Rn, where 2 less than or equal to p < n, q is subcritical exponent, i.e. p < p* - 1 = [n(p - 1) + p]/(n - p), sigma > 0. Applying the shooting argument, Schauder's fixed point theorem and some delicate estimates of auxiliary funtions, they study the influence of the parameters n, p, q, sigma > 0 on the existence, uniqueness and nonexistence of the radial ground state to the above p-laplacian equation.展开更多
The on-line estimation of the state of charge （SOC） of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial...The on-line estimation of the state of charge （SOC） of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial-basis-function neural network （RBF NN） has good characteristics to solve the nonlinear problem, a practical method for the SOC estimation of batteries based on the RBF NN with a small number of input variables and a simplified structure is proposed. Firstly, in this paper, the model of on-line SOC estimation with the RBF NN is set. Secondly, four important factors for estimating the SOC are confirmed based on the contribution analysis method, which simplifies the input variables of the RBF NN and enhttnces the real-time performance of estimation. FiItally, the pure electric buses with LiFePO4 Li-ion batteries running during the period of the 2010 Shanghai World Expo are considered as the experimental object. The performance of the SOC estimation is validated and evaluated by the battery data from the electric vehicle.展开更多
Following a sticky particle model and its computer simulation scheme proposed in the previous papers, the motions of particles in both 2-and 3-dimensional shear flow fields are simulated. At a steady state of clusteri...Following a sticky particle model and its computer simulation scheme proposed in the previous papers, the motions of particles in both 2-and 3-dimensional shear flow fields are simulated. At a steady state of clustering process, the radial distribution functions are calculated to precisely describe the microstructure of aggregates in dispersions, and the configuration of particles is displayed,which gives a direct view of microstructure. It is found that (1) the kind of the microstructure transforms from compact clusters to a loose network as the concentration of particles increases; (2) the microstructure is independent of shear rate which only dominates the size of clusters formed at steady state.展开更多
针对Model750控制力矩陀螺(control moment gyroscope,CMG)的耦合及扰动问题,提出一种基于径向基函数(radial basis function,RBF)神经网络逆系统和线性扩张状态观测器(linear extended state observer,LESO)的控制力矩陀螺复合解耦控...针对Model750控制力矩陀螺(control moment gyroscope,CMG)的耦合及扰动问题,提出一种基于径向基函数(radial basis function,RBF)神经网络逆系统和线性扩张状态观测器(linear extended state observer,LESO)的控制力矩陀螺复合解耦控制方法。利用神经网络的非线性逼近能力构建逆系统并与原系统串接,将原系统解耦成2个等效的伪线性子系统;采用线性扩张状态观测器估计等效系统的残余耦合项和扰动项加以补偿,并与比例微分(proportion differentiation,PD)控制器形成闭环以提高系统的动态控制性能。对提出的控制方法与PID-RBF逆控制方法进行仿真对比,结果表明:该方法可有效实现Model750系统的解耦,具有更好的动态控制性能和鲁棒性。展开更多
文摘In this paper, authors consider the existence, uniqueness and nonexistence of the radial ground state to the following p-laplacian equation: Delta(p)u + u(q) - \Du\(sigma) = 0, x epsilon Rn, where 2 less than or equal to p < n, q is subcritical exponent, i.e. p < p* - 1 = [n(p - 1) + p]/(n - p), sigma > 0. Applying the shooting argument, Schauder's fixed point theorem and some delicate estimates of auxiliary funtions, they study the influence of the parameters n, p, q, sigma > 0 on the existence, uniqueness and nonexistence of the radial ground state to the above p-laplacian equation.
基金Project supported by the National High Technology Research and Development Program of China (Grant No. 2011AA110303)the Beijing Municipal Science & Technology Project,China (Grant No. Z111100064311001)
文摘The on-line estimation of the state of charge （SOC） of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial-basis-function neural network （RBF NN） has good characteristics to solve the nonlinear problem, a practical method for the SOC estimation of batteries based on the RBF NN with a small number of input variables and a simplified structure is proposed. Firstly, in this paper, the model of on-line SOC estimation with the RBF NN is set. Secondly, four important factors for estimating the SOC are confirmed based on the contribution analysis method, which simplifies the input variables of the RBF NN and enhttnces the real-time performance of estimation. FiItally, the pure electric buses with LiFePO4 Li-ion batteries running during the period of the 2010 Shanghai World Expo are considered as the experimental object. The performance of the SOC estimation is validated and evaluated by the battery data from the electric vehicle.
文摘Following a sticky particle model and its computer simulation scheme proposed in the previous papers, the motions of particles in both 2-and 3-dimensional shear flow fields are simulated. At a steady state of clustering process, the radial distribution functions are calculated to precisely describe the microstructure of aggregates in dispersions, and the configuration of particles is displayed,which gives a direct view of microstructure. It is found that (1) the kind of the microstructure transforms from compact clusters to a loose network as the concentration of particles increases; (2) the microstructure is independent of shear rate which only dominates the size of clusters formed at steady state.
文摘针对Model750控制力矩陀螺(control moment gyroscope,CMG)的耦合及扰动问题,提出一种基于径向基函数(radial basis function,RBF)神经网络逆系统和线性扩张状态观测器(linear extended state observer,LESO)的控制力矩陀螺复合解耦控制方法。利用神经网络的非线性逼近能力构建逆系统并与原系统串接,将原系统解耦成2个等效的伪线性子系统;采用线性扩张状态观测器估计等效系统的残余耦合项和扰动项加以补偿,并与比例微分(proportion differentiation,PD)控制器形成闭环以提高系统的动态控制性能。对提出的控制方法与PID-RBF逆控制方法进行仿真对比,结果表明:该方法可有效实现Model750系统的解耦,具有更好的动态控制性能和鲁棒性。
