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Optimal Speed and Heading Control for Stabilization of Parametric Oscillations in Ships

  • Dominik A. BreuEmail author
  • Le Feng
  • Thor I. Fossen
Chapter

Abstract

In this chapter, two strategies to actively control ships experiencing parametric roll resonance are proposed. Both approaches aim at changing the frequency of the parametric excitation by controlling the Doppler shift, which, in recent results, has been shown to be effective to reduce the roll angle significantly. However, exactly how to change the frequency of the parametric excitation to stabilize the parametric oscillations has remained an open research topic. Thus, two optimal control philosophies that alter the ship’s forward speed and heading angle, which in turn changes the encounter frequency and consequently the frequency of the parametric excitation, are presented. This is referred to as frequency detuning. As a first approach, the methodology of extremum seeking (ES) control is adapted for ships in parametric roll resonance to iteratively determine the optimal setpoint of the encounter frequency in order to avoid one of the conditions for parametric roll. The encounter frequency is consequently mapped to the ship’s forward speed and heading angle by a control allocation. This is formulated as a constrained nonlinear optimization problem. Second, the use of a model predictive control (MPC) is proposed. By addressing both states and input constraints explicitly, the MPC formulation is used to change the ship’s forward speed and heading angle in an optimal manner to reduce parametric roll oscillations. The effectiveness of the proposed control approaches to stabilize parametric oscillations, by simultaneously changing the ship’s forward speed and heading angle optimally, is illustrated by computer simulations. This clearly verifies the concept of frequency detuning.

Keywords

Model Predictive Control Roll Angle Sideslip Angle Encounter Frequency Control Allocation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work was funded by the Centre for Ships and Ocean Structures (CeSOS), NTNU, Norway and the Norwegian Research Council.

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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Centre for Ships and Ocean StructuresNorwegian University of Science and TechnologyTrondheimNorway
  2. 2.Department of Engineering CyberneticsNorwegian University of Science and TechnologyTrondheimNorway

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