The Research And Implementation Of DTC System Based On Intelligent Algorithm

Direct torque control(DTC) technique which was put forth after Vector control technique is a novel control method for motor drives. With its simple control structure and excellent dynamic response, DTC technique has been a research focus in the AC drive fields. In this thesis, the improvement of low speed performance, the design of speed-sensorless schemes and the digital realization of DTC system are studieddetailedly.First, based on detailed analysis of the mathematical model of induction motor,theprinciple of the conventional DTC is studied deeply.In DTC scheme, the torque of motor is calculated and controlled in the stator reference frame. By using stator field orientation, the optimum switching mode is achieved to control the flux and torque of motor precisely and quickly. In this paper, double regenerative flux and torque controllers substitute single regenerative flux and torque controllers of the traditional DTC.Secondly, In view of the traditional DTC system, the use of the speed sensor such as cost, maintenance and the complex environment of stability is inadequate. By using nonlinear neural network mapping capabilities, speed information can be known exactly with a BP neural network. Simulation results show that this method has a high dynamic performance and stability accuracy, and the system has the ability to accurately track rotor speed. Speed sensor can be replaced in the system.Finally,the system scheme of speed sensorless DTC based on the speed adaptive flux observer is studied deeply with the chip of TMS320LF2407A DSP as the core controller. Both the hardware and software concerned are presented systematically.