Neural-Network-Based Low-Speed-Damping Controller for Stepper Motor With an FPGA

We present a low-speed-damping controller for a stepper motor using artificial neural networks (ANNs). This controller is designed to remove nonlinear disturbance at low speeds. The proposed controller improves the stepper motor performance at less than the resonance speed of the stepper motor system. Due to its ability to learn, the proposed controller can adapt to different resonant speed ranges without any identification process for system parameters. Conversely, we also introduce the implementation of an ANN-based controller, online backpropagation learning, and a microstep driver on a single field-programmable gate array. An implementation and experimental results are conducted to verify the feasibility and the effectiveness of the proposed controller.