In this paper, it presents a project of a fuzzy controller and a neural estimator to control a coordinate table powered by three-phase induction motor, aiming to implement an intelligent milling system. The position/s...In this paper, it presents a project of a fuzzy controller and a neural estimator to control a coordinate table powered by three-phase induction motor, aiming to implement an intelligent milling system. The position/speed control is performed using vector techniques of three-phase induction machines. The estimation of the motor electromagnetic torque is used for setting the feedrate of the table. The speed control is developed using TS （Takagi-Sugeno） fuzzy logic model and electromagnetic torque estimation using neural network type LMS （least mean square） algorithm. The induction motor is powered by a frequency inverter driven by a DSP （digital signal processor）. Control strategies are implemented in DSP. Simulation results are presented for evaluating the performance of the system.展开更多
文摘In this paper, it presents a project of a fuzzy controller and a neural estimator to control a coordinate table powered by three-phase induction motor, aiming to implement an intelligent milling system. The position/speed control is performed using vector techniques of three-phase induction machines. The estimation of the motor electromagnetic torque is used for setting the feedrate of the table. The speed control is developed using TS （Takagi-Sugeno） fuzzy logic model and electromagnetic torque estimation using neural network type LMS （least mean square） algorithm. The induction motor is powered by a frequency inverter driven by a DSP （digital signal processor）. Control strategies are implemented in DSP. Simulation results are presented for evaluating the performance of the system.
