Ac Motors Single Neuron Model Reference Adaptive Control System

This thesis is focused on the theory and simulation implementation of a single neuron model reference adaptive control (MRAC) controller provided with the ability of self-learning and self-adaptive, with the induction motor and Permanent Magnet Synchronous Motor (PMSM) taken as a control plant.Firstly, the math model of induction motor and PMSM, basic knowledge of neural networks and MRAC, theory of field-oriented control and hysteresis-current controlled PWM are introduced particularly, and the structure of vector control and neural networks MRAC is also provided.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, the neuron MRAC approach is proposed in place of traditional PID controller, making use of it's self-learning ability. A new error function is presented to avoid the affection of big-inertia of AC machines and even more to ensure better motor speed tracking efficiency.Based on the theory, then the simulation system of induction motor and PMSM based on MATLAB is proposed. The simulation results demonstrate that the scheme can ensure the motor speed tracking the reference speed quickly and precisely, and reduce its sensitivity to load disturbances. Furthermore, the algorithm is also robust for induction motor parameters variation. The designed controller is so simple that it is very easy to be implemented and applied.