Based on Neuron Adaptive Controller for Linear Motor Slip Frequency Vector Control System

It is hard to set up precise mathematic model of Linear Induction Motor, and the conventional PI vector control is hard to get desirable control effect, it is proposed to apply the neuron net control method into the slip frequency vector control system of the Linear Induction Motor. In this thesis, the neuron adaptive controller is used as speed controller, and conventional PI controller as current controller. The neuron net has ability to self-learning and adaptation, so its application into control does not depends on mathematic model of control object, and the neuron net controller can overcome the defect of less robustness of the conventional PI controller while changing of motor parameters. The simulation experiment has been made under the condition of starting and loading of motor. The results has shown that Linear Motor Slip Frequency Vector Control System Based on Neuron adaptive Controller has not only good dynamic and stable performance, but also better robustness than PI control system.