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However, Unmanned Surface Vehicle (USV) systems frequently encounter disturbances and model inaccuracies, resulting in mismatches between predicted and actual behaviors. This paper proposes a Gaussian Process Model Predictive Control (GP-MPC) framework to address the challenges of trajectory tracking in USVs. The MPC framework comprises a nominal model based on kinematic analysis and a Gaussian process error model. The latter compensates for system inaccuracies using sampled data. Simulations and real-world tests are conducted to compare GP-MPC with conventional MPC. 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