This paper introduces a novel dual-domain high-precision tracking control scheme. It aims to enhance the tracking performance of a permanent magnet linear synchronous motor (PMLSM) by integrating iterative-domain and time-domain strategies, incorporating iterative learning control (ILC) and model predictive control (MPC). This framework primarily targets linear motor motion tasks characterized by periodic and repetitive features. First, to suppress disturbances and enhance motion precision, the proposed algorithm is designed from two perspectives. Subsequently, in the iterative domain, a newly devised historical information operator is introduced to improve the convergence performance of the ILC in handling repetitive disturbances. Then, in the time domain, to address random errors, a constrained MPC is employed. Finally, the proposed scheme effectively enhances tracking performance and has been validated through experiments.