On-line gain tuning using RFNN for linear synchronous motor

In this study an integral-proportional (IP) controller with on-line gain tuning using a recurrent fuzzy-neural-network (RFNN) is proposed to control a permanent magnet linear synchronous motor (PMLSM) drive system. First, the structure and operating principle of the PMLSM are described in detail. Second, an IP controller with gain-tuning using a RFNN is proposed to control the position of the moving table of the PMLSM achieve high-precision position control with robustness. The backpropagation algorithm is used to train the RFNN online. Then, an IP controller with gain tuning using a RFNN is implemented in a PC-based computer control system. Finally, the effectiveness of an IP controller with gain tuning using a RFNN controlled PMLSM drive system is demonstrated by some experimental results. Accurate tracking response and superior dynamic performance can be obtained due to the powerful on-line learning capability of the RFNN. Furthermore, an IP controller with gain tuning using a RFNN is robust with regard to parametric variations.