Induction motor servo drive using robust PID-like neuro-fuzzy controller

A robust PID-like neuro-fuzzy controller, which has an ability to compensate for parameter variation, is proposed and applied to the speed control of the indirect vector-controlled induction motor. The controller gains are adjusted on-line using the tuning algorithm based on an artificial neural network (ANN). And a variable learning rate algorithm is proposed to improve the tracking performance while keeping the robustness. Simulation and experimental results confirm that good dynamic performance and high robustness to parameter variation and disturbance can be achieved by means of the proposed controller.