Adaptive Current Controller Based on Neural Network and Double Phase Compensator for a Stepper Motor

In this paper, we propose an advanced approach to improve the accuracy of stepper motors based on an effective phase compensator. The proposed approach includes an adaptive current controller and an adaptive position controller. The proposed adaptive feed-forward proportional-resonant (AFPR) current control technique with a neural network (NN) is an improvement over the conventional proportional-integral algorithm. In which, the NN algorithm is applied to obtain the optimal parameters for the AFPR controller. This results in an enhancement of the stepper motor current tracking. In addition, the advanced position controller is a combination of a damping and a phase-compensated (D&PC) method to reduce steady-state error and variations during low-speed operation. The position tracking performance is further appreciated by applying an advanced proportional integral derivative (PID) controller during high-speed operation. A bandpass filter is designed to switch the motor between low- and high-speed operational regimes. The proposed approach does not require any changes in the structure of the hardware. Therefore, it can be widely applied to a diverse set of industrial applications. The performance of the proposed approach was validated by conducting experiments under practical conditions.