This article discusses the development of an efficient controller for precise tracking of Autonomous Surface Vehicles (ASVs) using dynamic inversion control techniques. ASVs face challenges from environmental disturbances such as waves and winds, which are crucial for industries like maritime transport and military operations requiring high navigation accuracy. This article proposes an approach that integrates classical control with intelligent methods to mitigate disturbances and correct modeling errors. Two distinct dynamic inversion controller-based methods are proposed: one utilizes a perceptron neural network with an adaptive term to compensate for modeling errors in the dynamic inversion controller, and the other employs fuzzy logic to adjust control parameters for modeling error reduction. The aim of these methods is to enhance system flexibility and path-tracking accuracy. Evaluation criteria include step response analysis and the ability to track complex paths, with comparisons against the conventional PID control method showing favorable results.

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