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The study mainly focuses on the development of three model-free control strategies including a simple proportional-integral-differential (PID) scheme, a fuzzy-neural-network (FNN) control and an adaptive control for the positioning of a hybrid magnetic levitation (maglev) system. In general, the lumped-parameters dynamic model of a hybrid maglev system can be derived from the energy balance. In practice, the mathematical model can not be established precisely because this hybrid maglev system is inherently unstable in the levitated direction, and the relationships between current and electromagnetic force are highly nonlinear. To cope with the unavailable dynamics, model-free control design is used to handle the system behaviour. In this study the experimental results of PID, FNN and adaptive control schemes for the hybrid maglev system are reported. As can be seen from performance comparisons, the adaptive control system yields favorable control performance superior to that of PID and FNN control systems. Moreover, it not only has a learning ability similar to that of FNN control but also the simple control structure of PID control.