RETRACTED ARTICLE: Nonlinear backstepping control design of LSM drive system using adaptive modified recurrent Laguerre orthogonal polynomial neural network

The good control performance of the permanent magnet linear synchronous motor (LSM) drive system is very difficult to achieve using linear controller because of uncertainty effects, such as ending-fictitious force. A backstepping approach is proposed to control the motion of the LSM drive system. With the proposed backstepping control system, the mover position of the LSM drive achieves good transient control performance and robustness. Although favorable tracking responses can be obtained by the backstepping control system, the chattering in the control effort is critical because of the large control gain. Because there are many nonlinear and time-varying uncertainties in the LSM drive systems, the nonlinear backsteping control system, which an adaptive modified recurrent Laguerre orthogonal polynomial neural network (NN) is used to estimate uncertainty, is thus proposed to reduce the chattering in the control effort and thereby enhance the robustness of the LSM drive system. In addition, the on-line parameter training methodology of the modified recurrent Laguerre orthogonal polynomial NN is based on the Lyapunov stability theorem. Furthermore, two optimal learning rates of the modified recurrent Laguerre orthogonal polynomial NN are derived to accelerate parameter convergence. Finally, comparison of the experimental results of the present study demonstrates the high control performance of the proposed control scheme.