Synergistic Adaptive Robust Fault-Tolerant Control for Linear Motors: A Fully Actuated System Reference Model Approach

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