Optimization techniques for tuning heading controllers of an autonomous inscale fast-ferry model

Authors:
Francisco J. Velasco;Elías Revestido;Eloy Lopez;Emiliano Moyano;M. Haro Casado
Affiliations:
Dept. Electronic Technology & Systems Engineering and Automatic Control, Univ. Cantabria, Santander, Cantabria, Spain;Dept. Electronic Technology & Systems Engineering and Automatic Control, Univ. Cantabria, Santander, Cantabria, Spain;Dept. Sciences and Navigation Techniques, Engines and Naval Architecture, Univ. País Vasco, EHU, E.T.S. de Náutica y Máquinas Navales, Portugalete, Vizcaya, Spain;Dept. Applied Mathematics and Computing Sciences, Univ. Cantabria, E.T.S. de Ingenieros Industriales y Telecomunicaciones, Santander, Cantabria, Spain;Dept. Systems Engineering and Automatic Control, Electronic Technology & Electronics, University of Cáádiz, Faculty of Nautical Sciences, Cádiz, Spain
Venue:
SMO'08 Proceedings of the 8th conference on Simulation, modelling and optimization
Year:
2008

Citing 3
Cited 0

On the convergence of interior-reflective Newton methods for nonlinear minimization subject to bounds

Mathematical Programming: Series A and B
Genetic Algorithms: Concepts and Designs with Disk

Genetic Algorithms: Concepts and Designs with Disk
Autopilot and track-keeping simulation of an autonomous in-scale fast-ferry model

ICS'08 Proceedings of the 12th WSEAS international conference on Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

In the present paper, optimization techniques are used to tune heading controllers. Several simulations point out the improvement obtained with this short of techniques with respect to other. The heading control is based on a model obtained from data provided by a platform for marine vehicles. This platform has an in-scale fast-ferry model TF-120, which was developed to be autonomous and is controlled remotely from a PC using Wi-Fi communications. Different classical control structures for heading autopilot are studied and results obtained from optimization techniques are compared to genetic algorithm techniques.