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In this paper, a direct adaptive radial basis function (RBF) neural network control algorithm is presented for a class of ship course with uncertain discrete-time nonlinear systems. To aviod some system states that are unmeasurable and make the adaptive control approach more universal and convenient to be implemented in practical application, the original ship course with uncertain discrete-time nonlinear system is transformed into the form of the input-output model. According to the input-output model, a direct adaptive RBF NN control for the ship course with discrete-time nonlinear system is carried out based on the existence of the implicit desired feedback control (IDFC). In the controller design process, RBF neural networks are used to emulate the desired desired feedback control and approximate the unknown function. The stability of the closed-loop system is proven to be uniformly ultimately bounded (UUB) by using lyapunov theorem, and tracking error can converge to a small neighborhood of zero by choosing the design parameters appropriately. In the end, the simulation example of motor vessel “yukun” is employed to illustrate the effectiveness of the proposed algorithm.
Marine Engineering Frontiers – Science and Engineering Publishing Company
Published: Aug 1, 2013
Keywords: Ship Course, Discrete-Time Nonlinear System, RBF Neural Network
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