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 timevarying, 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 highperformance industrial applications. The effectiveness of the proposed control scheme is verified by some experimental results.