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Direct Adaptive Neural Network Control for A Class of Ship Course Uncertain Discrete-time Nonlinear Systems

Direct Adaptive Neural Network Control for A Class of Ship Course Uncertain Discrete-time... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Marine Engineering Frontiers Science and Engineering Publishing Company

Direct Adaptive Neural Network Control for A Class of Ship Course Uncertain Discrete-time Nonlinear Systems

Marine Engineering Frontiers , Volume 1 (3) – Aug 1, 2013

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Publisher
Science and Engineering Publishing Company
Copyright
Science and Engineering Publishing Company
ISSN
2327-722X
eISSN
2327-7653

Abstract

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.

Journal

Marine Engineering FrontiersScience and Engineering Publishing Company

Published: Aug 1, 2013

Keywords: Ship Course, Discrete-Time Nonlinear System, RBF Neural Network

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