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Résumé : We investigated the potential of multi-layer extreme learning machines (MLELMs) for trajectory tracking in autonomous unmanned vehicles, focusing on an unmanned surface vehicle (USV). MLELMs were used to design position and attitude controllers within a two-loop control architecture, ensuring that the USV accurately follows a reference trajectory. The performance of an MLELM-based controller in a tracking task was evaluated via numerical simulations of a USV dynamic model governed by nonlinear equations. The computational results confirmed the feasibility of the MLELM to accomplish this task with appropriate accuracy, demonstrating its potential applicability in control systems.