Robust Control of PM Spherical Stepper Motor Based on Neural Networks

There are many uncertainties and disturbances in the real dynamic system of a spherical stepper motor that make traditional control methods with lower precision, such as uncertain changes of magnetic field, load, and friction that generate speed ripple and deteriorate the 3-D tracking performance of the spherical motor system. In this paper, an available method is proposed to solve them by using neural networks (NNs) and a robust control scheme for improving the performance. First, a simplified torque calculation model based on finite-element method results can guarantee quick prediction of electromagnetic torque with lower error. Thus, the system model considering the friction, load, and disturbances is developed. Second, a robust NN (RNN) control scheme is presented to eliminate uncertainties to improve the tracking robust stability and overcome the undesired influence of uncertainties based on the nonlinear system dynamic model under continuous-trajectory tracking mode. Finally, as an example, the step-response and continuous-tracking processes of the motor using an RNN controller are simulated, and experiments, including the tracking using RNN proportional-differential control, are carried out to confirm the usefulness of the proposed control scheme. The simulation and experimental results of the proposed control scheme on the spherical stepper motor system demonstrate the effectiveness on satisfactory tracking performance.