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Since the dynamic characteristics of a linear piezoelectric ceramic motor (LPCM) are highly nonlinear and time varying, it is difficult to design a suitable motor drive and position controller that realizes accurate position control at all time. This study investigates a double-inductance double-capacitance (LLCC) resonant driving circuit and a sliding-mode fuzzy-neural-network control (SMFNNC) system for the motion control of an LPCM. First, the motor structure and LLCC driving circuit of an LPCM are introduced. The LLCC resonant inverter is designed to operate at an optimal switching frequency such that the output voltage will not be influenced by the variation of quality factor. Moreover, a SMFNNC system is designed to achieve favorable tracking performance without precise dynamic models being controlled. All adaptive learning algorithms in the SMFNNC system are derived in the sense of Lyapunov stability analysis, so that system-tracking stability can be guaranteed in the closed-loop system. The effectiveness of the proposed driving circuit and control system is verified by experimental results.