Adaptive backstepping control of primary permanent magnet linear motor via radial basis function neural network and command filter

This paper studies the displacement and speed control of primary permanent magnet linear motor (PPMLM) under the influence of unmodelled load interference, time-varying parameters and end effects. Adaptive backstepping control is first proposed to ensure system stability in the presence of time-varying parameters during motor movements. Then, radial basis function (RBF) neural network is used to compensate for the effect of the unmodeled load disturbance of the system. Furthermore, the command filter is implemented in the backstepping control to counteract the differential expansion phenomenon. The compensation signal is set to reduce the filtering error induced by the filter. Simulation results are given to verify the good performance of the proposed control in comparison with the adaptive backstepping controller and the command filter adaptive backstepping controller.