Linear permanent magnet synchronous motor drive system using AAENNB Control system with error compensation controller and CPSO

Due to nonlinear friction and uncertainty effects of linear permanent magnet synchronous motor (LPMSM), the existing linear controllers cannot achieve good control performance. In order to increase the robustness of system under some uncertainties disturbances, the proposed adaptive amended Elman neural network backstepping (AAENNB) control system with error compensation controller is adopted for controlling the LPMSM drive system. Firstly, the field-oriented control (FOC) is applied to formulate the dynamic equation of the LPMSM drive system. Secondly, a backstepping approach is proposed for controlling the motion of LPMSM drive system. The proposed backstepping control system and the mover position of the LPMSM drive system possess good transient control performance under the uncertainties action for the tracking of periodic references. Because of the LPMSM with nonlinear and time-varying dynamic characteristics, an adaptive amended Elman neural network uncertainty observer (AAENNUO) with the adaptive law is proposed to estimate the required lumped uncertainty. Moreover, the error compensation controller with the error estimation law is proposed to compensate the minimum reconstructed error according to Lyapunov stability theorem. Furthermore, to improve convergent speed and to obtain better learning performance, the varied learning rate of the weight in the AENN is regulated by use of the corrected particle swarm optimization (CPSO) algorithm with segment regulation mechanics, that is, the innovativeness for using the CPSO algorithm. At last, the usefulness of the proposed control system is confirmed by experimental results.