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A field-programmable gate array (FPGA) based computed force control system using an Elman neural network (ENN) is proposed to control the mover position of a linear ultrasonic motor (LUSM) in this paper. First, the structure and operating principle of the LUSM are introduced. Then, the dynamics of the LUSM mechanism with the introduction of a lumped uncertainty, which include the friction force, are derived. Since the dynamic characteristics and motor parameters of the LUSM are nonlinear and time varying, a computed force control system using ENN is designed to improve the control performance for the tracking of various reference trajectories. The ENN with both online learning and excellent approximation capabilities is employed to estimate a nonlinear function including the lumped uncertainty of the moving table mechanism. Moreover, the Lyapunov stability theorem and the projection algorithm are adopted to ensure the stability of the control system and the convergence of the ENN. Furthermore, an FPGA chip is adopted to implement the developed control algorithm for possible low-cost and high-performance industrial applications. The experimental results show that excellent positioning and tracking performance are achieved, and the robustness to parameter variations and friction force can be obtained as well using the proposed control system.