High-precision, high-reliability control of linear motors is crucial for intelligent manufacturing. However, existing methods are hindered by insufficient adaptive convergence, reliance on known uncertainty bounds, dependence on reference trajectory derivatives, and vulnerability to actuator faults. To overcome these limitations, a control scheme is proposed that synergistically integrates fixed-gain robust control and neural networks. To handle discontinuous reference signals, an ideal model is presented as a trajectory planner to provide the necessary derivatives based on the fully actuated system approach. The proposed synergistic controller does not require knowledge of uncertainty bounds in the stability analysis and effectively handles time-varying actuator faults. Experiments on a linear motor platform verify its effectiveness, demonstrating that the design ensures high precision even with a reduced number of neurons and in the presence of strong disturbances.