An Elman neural network control system for linear piezoelectric ceramic motor using FPGA

An Elman neural network (ENN) control system is proposed in this study to control the mover position of a linear piezoelectric ceramic motor (LPCM) using field-programmable gate array (FPGA). First, the structure and operating principle of the LPCM are introduced. Since the dynamic characteristics and motor parameters of the LPCM are nonlinear and time-varying, an ENN control system is designed to achieve precision position control. The network structure and on-line learning algorithm using delta adaptation law of the ENN are described in detail. Moreover, an FPGA chip is adopted to implement the developed control algorithm for possible low-cost and high-performance industrial applications. The effectiveness of the proposed control scheme is verified by some experimental results.