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 online learning capability of the RFNN. Furthermore, an IP controller with gain tuning using a RFNN is robust with regard to parametric variations