The position control system of servo motors plays a critical role in achieving high-precision and reliable actuation in robotic applications. This study focuses on developing a prescribed-performance robust control strategy for permanent magnet synchronous motor position regulation under uncertainties. An adaptive robust controller (ARC) is, firstly, designed by means of a bounded estimation technique to address parameter uncertainties in servo systems. The controller gains and adaptive law are derived through a rigorous stability analysis, ensuring guaranteed stable position tracking. Meanwhile, a prescribed performance approach is constructed based on control barrier functions (CBFs), enabling real-time correction of the control input via a quadratic programming optimization to guarantee prescribed transient and steady-state tracking performance. Furthermore, the robust safety of the proposed CBF-based ARC method is demonstrated by quantitatively bounding the parameter estimation errors. Compared with existing methods, simulation and experimental results are conducted to confirm the effectiveness and superior performance of the proposed method.

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