The Vector Control System Of Asynchronous Motor Based On Single Neuron

The AC motor drive has been one of the important areas in automation research. The performance of the AC motor drive has been highly improved recently. The thesis dissertates on the principles of vector control and field orientation at first. The vector control can improve the torque control performance of the AC motor drive, but due to some uncertain reasons during the control process, the controller's dynamic performance of the AC drive will become bad. The traditional PID controller can not satisfy the requests of high-performance control, such as accurate speed or torque control and robustness.The intelligent control based on neural network offers an effective control method for high-performance AC drive systems. On the basis of analyzing in depth the principle of the neural network, the paper has researched on the applications of neural network in the AC motor drive. In allusion to the difficulty of parameters adjustment in traditional PID controller and the dis-commodiousness of constant modify when motor's parameters or environment changes, a kind of single neuron PID is proposed in place of traditional PID controller, making use of it self-learning ability. Based on PSD algorithm, the paper proposes the gain adaptive algorithm, to make the gain of the cotroller have self-adaptive ability.Based on the theory, then a full digital AC vector control speed adjustment system based on TI's DSP is designed. In it TI's DSP is used as the central process unit, and IPM is used in the power supply unit. The digitally realized methods of single neuron PID controller and SVPWM are introduced detailedly. Based on Matlab/Simulink the performance parameters of the controller are analyzed. Finally, the performance of traditional PID and single neuron PID controller is compared. The simulation and experiment results show the single neuron PID can take the place of traditional PID controller and it has simple structure and can be implemented easily with strong self-adaptive ability and robustness.