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Indirect Rotor-Field-Oriented Control (IRFOC) techniques brought on a renaissance in modern high-performance control of PWM inverter fed SPIM. In this paper, an IRFOC system is proposed for SPIMs including a relatively simple and effective decoupling scheme. This is achieved by introducing two new decoupling signals to the system. However, model asymmetry in SPIMs causes extra coupling between two stator windings. To use the field orientation control, the asymmetry must be eliminated by using an appropriate variable changing. A computer simulation of the IRFOC for Single-Phase Induction Motor drive is carried out to test the validity of the proposed method at nominal and zero speed. The design, analysis, and implementation for a 800W Single-Phase Induction Motor are completely carried out using a dSPACE DS1103 digital signal processor (DSP) based real-time data acquisition control (DAC) system, and MATLAB/Simulink environment. Digital simulation and experimental results are presented to show the improvement in performance of the proposed algorithm.

in order to predict gas content of coal seam accurately in binchang mining, we use core data to build the BP neural network. We select the important controlling factors which impacted gas content of coal seam, coal bed thickness, ash and max vitrinite reflectance as the basic features of the BP neural network model, and establish the BP neural network prediction model between coal bed methane content and the main controlling factors. The testing results show that the BP neural network model could truly reflect the non-linear relationship between the gas content and the controlling factors, and obtain minimal error between the predicted results and the measured ones. This method provides the probability for using geological, logging and seismic information to predict gas content of coal seam. Key word- CBM content, geologic parameter, BP neural networks model