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A field-programmable gate array (FPGA)-based intelligent-complementary sliding-mode control (ICSMC) is proposed in this paper to control the mover of a permanent magnet linear synchronous motor (PMLSM) servo-drive system to track periodic-reference trajectories. First, the dynamics of the field-oriented control PMLSM servo drive with a lumped uncertainty, which contains parameter variations, external disturbances, and nonlinear-friction force, is derived. Then, to achieve the required high-control performance, the ICSMC is developed. In this approach, a radial-basis function-network (RBFN) estimator with accurate approximation capability is employed to estimate the lumped uncertainty directly. Moreover, the adaptive-learning algorithms for the online training of the RBFN are derived using the Lyapunov theorem to guarantee the closed-loop stability. Furthermore, the FPGA chip is adopted to implement the developed control and online learning algorithms for possible low-cost and high-performance industrial applications using PMLSM. Finally, some experimental results are illustrated to show the validity of the proposed control approach.