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, Bled, Slovenia, 1995, pp. 269-274.

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, Professor 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