Field-programmable gate array-based recurrent wavelet neural network control system for linear ultrasonic motor

A field-programmable gate array (FPGA)-based recurrent wavelet neural network (RWNN) control system is proposed to control the mover position of a linear ultrasonic motor (LUSM). First, the structure and operating principles of the LUSM are introduced. Since the dynamic characteristics and motor parameters of the LUSM are non-linear and time-varying, an RWNN controller is designed to improve the control performance for the precision tracking of various reference trajectories. The network structure and its on-line learning algorithm using delta adaptation law of the RWNN are described in detail. Moreover, the connective weights, translations and dilations of the RWNN are trained on-line. Furthermore, to guarantee the convergence of the tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RWNN. In addition, an FPGA chip is adopted to implement the developed control algorithm for possible low-cost and high-performance industrial applications. Finally, the effectiveness of the proposed control system is verified by some experimental results.