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The periodicity of repetitive contouring tasks is utilized in this paper to synthesize an adaptive robust repetitive contouring controller (ARRC) for an industrial biaxial precision gantry to further improve the achievable contouring performance in practice. Specifically, repetitive-control-input-like terms are introduced to learn unknown but periodic nonlinearities when performing repetitive tasks. Physically, intuitive discontinuous projection modifications to the adaptation law are used to ensure all the on-line estimates within their known bounds. Robust control terms are also constructed to effectively attenuate the effect of model compensation errors due to various uncertainties including nonperiodic disturbances for a theoretically guaranteed transient performance and steady-state tracking accuracy in general. Comparative experiments are carried out on an industrial linear-motor-driven biaxial precision gantry. The experimental results show that the proposed ARRC controller not only achieves the best nominal contouring performance but also possesses strong performance robustness to large disturbances, which confirms the effectiveness of the proposed ARRC scheme in practical contouring applications.