A Speed Control System of Permanent Magnet Linear Synchronous Motor Using Neuron Adaptive 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. It is proposed to apply the neuron net control method into control system of the permanent magnet linear synchronous motor (PMLSM). In this thesis, a neuron adaptive controller is designed according to nonlinear and uncertainty of the PMLSM as speed controller. The dynamic equation of the PMLSM feeding system is obtained by analyzing PMLSM d-q model. The simulation experiment has been made under the condition of starting and loading of motor. The results has shown that PMLSM Control System Based on Neuron adaptive Controller has not only good dynamic and stable performance, but also better robustness than PI control system.