Underactuated systems like sea vessels have degrees of motion that are insufficiently matched by a set of independent actuation forces . An online machine learning mechanism based on integral reinforcement learning is proposed to find a solution for a class of nonlinear tracking problems with partial prior knowledge of the system dynamics . The solution is implemented using an online value iteration process which is realized by employing means of theadaptive critics and gradient descent approaches . The adaptive learningmechanism exhibited well-functioning and interactive features in react to different desired reference-tracking scenarios, such as react to desired reference tracking scenarios. The adaptivelearning mechanism exhibited well functioning and responsive features in reacting to different reference- tracking scenarios .

Author(s) : Mohammed Abouheaf, Wail Gueaieb, Md. Suruz Miah, Davide Spinello

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Keywords : tracking - scenarios - reference - learning - mechanism -

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