Adaptive Control for Robotic Manipulators base on RBF Neural Network

An adaptive neural network controller is brought forward by the paper to solve trajectory tracking problems of robotic manipulators with uncertainties.  The  first  scheme consists of  a PD feedback  and  a  dynamic  compensator  which is  composed by  neural  network controller and  variable  structure controller.  Neutral network controller is designed to adaptive learn and compensate the unknown uncertainties, variable   structure controller is designed to eliminate approach errors of neutral network. The adaptive weight learning algorithm of neural network is designed to ensure online real-time adjustment, offline learning phase is not need; Global asymptotic stability (GAS) of system base on Lyapunov theory is analysised to ensure the convergence of the algorithm. The simulation results show that the kind of the control scheme is effective and has good robustness

Effect of Na Doping on the Nanostructures and Electrical Properties of ZnO Nanorod Arrays

The p-type ZnO nanorod arrays were prepared by doping Na with hydrothermal method. The structural, electrical, and optical properties were explored by XRD, Hall-effect, PL, and Raman spectra. The carrier concentrations and the mobility of Na-doped ZnO nanorod arrays are arranged from 1.4×1016 cm−3 to 1.7×1017 cm−3 and 0.45 cm2 v−1 s−1 to 106 cm2 v−1 s−1, respectively

Asymptotic behavior of solutions for the thermoviscous acoustic systems

We study some asymptotic properties of solutions for the acoustic coupled systems in thermoviscous fluids which was proposed by [Karlsen-Bruus, \emph{Phys. Rev. E} (2015)]. Basing on the WKB analysis and the Fourier analysis, we derive optimal estimates and large time asymptotic profiles of the energy term via diagonalization procedure, and of the velocity potential via reduction methodology. We found that the wave effect has a dominant influence for lower dimensions comparing with thermal-viscous effects. Moreover, by employing suitable energy methods, we rigorously demonstrate global (in time) inviscid limits as the momentum diffusion coefficient vanishes, whose limit model can be regarded as the thermoelastic acoustic systems in isotropic solids. These results explain some influence of the momentum diffusion on asymptotic behavior of solutions

Self-learning PID Control for X-Y NC Position Table with Uncertainty Base on Neural Network

An adaptive radical basis function (RBF) neural network PID control scheme for X-Y position table is proposed by the paper. Firstly, X-Y position table model is established, controller based on neutral network is used to learn adaptive and compensate uncertainty model of X-Y position table, neutral network is used to study model. PID neural network controller base on augmented variable method is designed. PID controller is used as assistant direction error controller, neural network parameters base on stochastic gradient algorithm can be adjust adaptive on line. The simulation results show that the presented controller has important engineering value

Fault diagnosis using an improved fusion feature based on manifold learning for wind turbine transmission system

In this paper, a novel fault diagnosis method based on vibration signal analysis is proposed for fault diagnosis of bearings and gears. Firstly, the ensemble empirical mode decomposition (EEMD) is used to decompose the vibration signal into several subsequences, and a multi-entropy (ME) is proposed to make up the fusion features of the vibration signal. Secondly, an improved manifold learning algorithm, local and global preserving embedding (LGPE), is applied to compress the high-dimensional fusion feature set into a two-dimension feature set. Finally, according to the clustering accuracy of different feature set, the fault classification and diagnosis can be performed in the reduced two-dimension space. The performance of the proposed technique is tested on the fault of wind turbine transmission system. The application results indicate that the proposed method can achieve high accuracy of fault diagnosis

The Link Between Parental Absence and Poor Reading Comprehension: Evidence From the Left-Behind Children in Rural China

Family environment affects children's reading comprehension ability. In poverty-stricken parts of rural China, some parents have been migrant workers for many years. It is common for left-behind children to have one or both parents permanently missing. This study explores the impact of parental absence on children's reading comprehension. We measured the reading comprehension of 903 children (467 left-behind and 436 parented), using the Chinese Primary School Students' Reading Comprehension Ability Scale. After controlling for the parents' levels of education, reading input, and interest variables, we found that children with absent parents had significantly lower reading comprehension scores than parented children; they struggled to understand chapter layout, the author's intentions, writing technique, evaluation, and appreciation. While children with absent fathers received the same scores as parented children, those with absent mothers received significantly lower scores. Clearly, absent mothers have a greater impact on children's reading comprehension than absent fathers