Sliding Mode Backstepping Control of Induction Motor Based on Self-recurrent Wavelet Neural Networks
基于自回归小波神经网络的感应电动机滑模反推控制

A new decoupled induction motor model is introduced for enhancing the robustness of control. Sliding mode control and backstepping control are applied to virtual torque and flux linkage voltage controller designs based on induction motor decoupled model. The magnitude of sliding mode switching gain is the key reason causing system chattering. Self-recurrent wavelet neural networks (SRWNN) is used to estimate sliding mode switching gain on-line, which can reduce chattering caused by sliding mode control effectively. The results of simulation prove that the scheme of sliding mode backstepping control based on SRWNN on-line estimation of switching gain can enhance the robustness of induction motor control effectively and reduce the chattering caused by sliding mode control as well.