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.
Full text not currently available from Enlighten.
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|
|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|
Enlighten Editors: Update this record