Adaptive Inverse Induction Motor Control

The adaptive inverse control initiated by B.Widrow is a very innovative control method, it uses the inverse of the controlled plant as the cascaded controller to control the system dynamics, and lets output follow the reference input. Simultaneously it also can control the system dynamics and the plant disturbance separately. Robustness of high-performance induction motor has been greatly affected by the uncertainty of the motor parameters. So the parameter related robustness of the system is a key issue to be talked. This thesis utilizes the adaptive inverse control method to improve the performance of the induction motor.The contents of this thesis are as follows:1. Aim at the problem in the traditional and classic LMS adaptive filtering; readjust the weight update law, this thesis proposes varying region, and variable step size LMS algorithm. The algorithm can improve the convergent speed, time-varying track ability and convergent precision.2. This thesis reconfigures the basis frame work of adaptive inverse control. It controls the dynamic and static performance of the system. It combines the inverse controller and the adaptive theory to strengthen the system performance.3. This thesis extends the adaptive inverse control to nonlinear system, and uses it to control the induction machine. Because the parameters of induction motor are uncertainty, and time-varying variables; the artificial neural network is introduced into the plant modeling and inverse modeling. We propose a varying region and variable step size BP algorithm to improve the system performance.4. The simulations are carried out with Matlab. The experiment results demonstrate the advantages of the adaptive inverse control strategies.