Permanent magnet linear synchronous motors (PMLSMs) are main power source of many electric vehicles, which are widely used in maglev trains, light rail transit, subways and other electric trail vehicles. However, prolonged operation may induce various actuator and sensor faults, including demagnetization, wear, reduction in electromechanical constants, or decline of component performance. This will bring great threat to the safety of rail vehicles. This article addresses synchronization control of PMLSM-based dual-linear-motor synchronous-driven (DLMSD) systems under actuator and sensor faults, external disturbances, and model uncertainties. A radial basis function neural network (RBFNN) adaptive fault-tolerant control (AFTC) scheme (NN-AFTC) based on a cross-coupling structure is proposed to comply with the strong requirements for tracking precision and synchronization performance of these systems, also providing excellent fault tolerance and disturbance rejection. Specifically, AFTC compensates for actuator and sensor faults, whereas the NN controller estimates and compensates for system residual and unmodeled errors, and unknown disturbance. Comparative experiments with several latest control strategies have been conducted on an actual DLMSD platform. The obtained results provide clear evidence of the superiority and efficacy of the proposed NN-AFTC approach.
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