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Ship steering control by neural networks trained using feedback linearization control laws

Simensen, R., and Murray-Smith, D.J. (1995) Ship steering control by neural networks trained using feedback linearization control laws. In: IFAC/IMACS International Workshop on Artificial Intelligence in Real-Time Control, 1995, Bled, Slovenia.

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Abstract

One problem of ship steering systems is that the dynamics of the vessel are dependent on the forward speed. Since artificial neural networks can provide a nonlinear controller which performs well for a wide range of plant dynamics, such networks are of potential interest for ship steering applications. This paper describes simulation studies in which a feed-forward network is trained to behave like a feedback linearization controller. Results suggest that the approach can yield a control system having a satisfactory level of performance for a range of operating conditions. The choice of network configuration and training data sets are, however, of considerable importance.

Item Type:Conference Proceedings
Additional Information:Published in J. Kocijan and R. Karba (editors) Proceedings 1995 IFAC/IMACS International Workshop on Artificial Intelligence in Real-Time Control, IFAC, 1995.
Keywords:Neural networks, ship control, feedback linearization, backpropagation algorithm
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Murray-Smith, Prof David
Authors: Simensen, R., and Murray-Smith, D.J.
Subjects:Q Science > QA Mathematics > QA76 Computer software
T Technology > TJ Mechanical engineering and machinery
T Technology > TK Electrical engineering. Electronics Nuclear engineering
V Naval Science > VM Naval architecture. Shipbuilding. Marine engineering
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy

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