A linear permanent magnet synchronous motor drive system is existed in many nonlinear effects such as the external load force, the flux saturation, the cogging force, the column friction and Stribeck force, and the parameters variations. Due to the uncertainty effects, the linear permanent magnet synchronous motor drive system is hard to achieve the good control performance by using linear controller. To raise robustness under occurrence of uncertainty, the integral backstepping control system with hitting function is first proposed for controlling the linear permanent magnet synchronous motor drive system. The used integrator can ameliorate the system’s robustness under the parameters uncertainties and external force disturbances. To reduce vibration of control strength, the integral backstepping control system by means of the revised recurrent fuzzy neural network with mended particle swarm optimization is next proposed to operate the linear permanent magnet synchronous motor drive system to raise robustness of system. Furthermore, four variable learning rates in the weights of the revised recurrent fuzzy neural network are adopted by using mended particle swarm optimization to speed up parameter’s convergence. Finally, comparative performances through some experimental upshots are verified that the integral backstepping control system by means of revised recurrent fuzzy neural network with mended particle swarm optimization has better control performances than those of the proposed methods for the linear permanent magnet synchronous motor drive system.
Probabilistic, Recurrent, Fuzzy Neural Network for Processing Noisy Time-Series Data
