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

Kondo correlation and spin-flip scattering in spin-dependent transport through a quantum dot coupled to ferromagnetic leads

We investigate the linear and nonlinear dc transport through an interacting quantum dot connected to two ferromagnetic electrodes around Kondo regime with spin-flip scattering in the dot. Using a slave-boson mean field approach for the Anderson Hamiltonian having finite on-site Coulomb repulsion, we find that a spin-flip scattering always depresses the Kondo correlation at arbitrary polarization strength in both parallel and antiparallel alignment of the lead magnetization and that it effectively reinforces the tunneling related conductance in the antiparallel configuration. For systems deep in the Kondo regime, the zero-bias single Kondo peak in the differential conductance is split into two peaks by the intradot spin-flip scattering; while for systems somewhat further from the Kondo center, the spin-flip process in the dot may turn the zero-bias anomaly into a three-peak structure.Comment: 4 pages, 2 figure