Application of Neural Networks to Rudder Roll Stabilization

Application of neural networks to rudder roll stabilization

A.Tiano ISTITUTO AUTOMAZIONE NAVALE C.N.R. Vi a le causa 18R 16145 Genova, Italy

Wei-wu Zhou MINISTRY OF TRANSPORTATION Government of British Columbia Victoria, B. C., Canada

Abstract

A number of research studies have been carried out, during the last two decades, devoted to rudder roll stabilization of surface ships. A possible solution has been recently proposed, which is based on adaptive control. According to such approach, the unknown system parameters are recursively estimated by an RPE (Recursive Prediction Error) method for a linear or non linear rudder-yaw and rudder-roll dynamical model, while an LQG (Linear Quadratic Gaussian) optimal control strategy is subsequently applied in order to minimize a preset cost function associated to the dynamical process. It should be pointed out, however, that in the presence of a relatively high number of unknown parameters, RPE method can give rise to time consuming numerical algorithms with a too low convergence rate. This paper outlines a possible alternative use of artificial neural network as an efficient recursive parameter estimation tool. For this purpose, a particular type of neural network called CINN (Competitively Inhibited Neural Network) is considered, which explicitely takes into account competition among neurons. The competition process is used for achieving adaptive features which are used to improve the efficiency of the estimation algorithm in terms of convergence rate and robustness. The performances of the resultant neural network based adaptive controller applied to the rudder roll stabilization problem are illustrated through a number of simulation examples.

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is pubLished because the fuLL paper is not arr ived in t i me

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