A note on ship-motion prediction based on wave-excitation input estimation

Chung, Junyeon; Bien, Zeungnam; Kim, Y.S.

doi:10.1109/48.107153

Cited by 24 publications

(13 citation statements)

References 4 publications

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“…If the principal frequencies are known the ship motion can be written as a superposition of N sinusoidal waves with different frequencies. In Chung et al [16] the sea excitation is extrapolated using this approach and the ship motion is predicted using the ship model.…”

Section: Related Researchmentioning

confidence: 99%

On the influence of ship motion prediction accuracy on motion planning and control of robotic manipulators on seaborne platforms

From

Gravdahl

Abbeel

2010

2010 IEEE International Conference on Robotics and Automation

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Abstract-Robotic manipulators on non-inertial platforms, such as ships, have to endure large inertial forces due to the non-inertial motion of the platform. When the non-inertial platform's motion is known, motion planning and control algorithms can eliminate these perturbations-in fact, in some situations the motion planning algorithms can even leverage the inertial forces to more cheaply move to a target point. However, for many non-inertial platforms, the motion is unknown.In this paper we investigate how prediction errors and the choice of the prediction horizon affect the motion planning and control of robots mounted on a non-inertial base with a particular focus on seaborne platforms. We study the following three aspects: (i) We study prediction of ship motion and how prediction errors affect the motion planning and control of the manipulator. (ii) We evaluate the relationship between prediction accuracy and control. In particular, we study what prediction horizon length is useful for motion planning and control. We also consider how uncertainties in the ship motion predictions map to uncertainties in the future state of the robot and how to include the variance in the cost function to increase the optimal horizon length. (iii) Finally, we study a receding horizon approach, which re-solves the optimal control problem on-line over a horizon as determined to be meaningful from (ii). Several simulations are presented and, to our knowledge, for the first time experiments of ship-manipulator systems based on real ship motion data are presented.

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Section: Related Researchmentioning

confidence: 99%

On the influence of ship motion prediction accuracy on motion planning and control of robotic manipulators on seaborne platforms

From

Gravdahl

Abbeel

2010

2010 IEEE International Conference on Robotics and Automation

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“…(8)(9)(10)(11) These methods are all based on an accurate mechanical model or accurate statistical parameters. However, in engineering, it is difficult to acquire an accurate input/output mechanical model for carriers because of the overcomplex relationship between wind/waves and a ship.…”

Section: Introductionmentioning

confidence: 99%

A Prediction Method for Deck Motion of Aircraft Carrier Based on Particle Swarm Optimization and Kernel Extreme Learning Machine

2017

Sensors and Materials

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The prediction of deck motion is an effective and potential means of improving the landing/ take-off safety of carrier-based aircraft using current and historical deck-motion measurements when deck motion in six degrees of freedom cannot be effectively controlled or restrained. The prediction models of deck motion should have excellent nonlinear fitting ability to cope with the deck-motion characteristics of randomness and nonlinearity caused by waves and wind; and should not use heavy computation to fulfill the requirement of real-time prediction for deck motion. It is generally believed that classical feed-forward neural networks, such as the back-propagation (BP) network, have excellent nonlinear fitting ability but suffer from slow training processes and reduced local optimum, thus failing to satisfy the requirements of real-time and high accuracy for deck-motion prediction. In addition, the extreme learning machine (ELM) is easy to train but it is difficult for ELM to determine the number of hidden layer nodes; an incorrect number of hidden layer nodes will introduce poor stability and generalization ability. To fulfill the requirements of deck motion prediction, a prediction method based on ELM, support vector machine, and particle swarm optimization [particle swam optimization kernel extreme learning machine (PSO-KELM)] is designed. In this method, the fundamental structure of the ELM is used and the kernel function from the support vector machine (SVM) is introduced to replace the hidden function in ELM. Further aiming at the acquisition of optimal parameters, including the penalty coefficient and kernel parameters for the kernel function, autoadaptive particle swarm optimization is adopted. Simulation results indicate that a prediction method based on PSO-KELM has the advantages of a simple structure, fast training speed, and powerful generalization ability, and thus can satisfy the requirements of real-time and high-accuracy deck-motion prediction. Compared with the prediction data from BP and the ELM, high-precision prediction data can be obtained with PSO-KELM. PSO-KELM has a significantly reduced training time compared with BP.

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“…When the ship is moving with forward speed, there exists a certain relation between the ac tual wave frequencies, and the frequencies encountered by the ship [6]. Following this relation, the direct influence of the waves on the ship is given as follows:…”

Section: Ship Motion Dynamicsmentioning

confidence: 99%

“…The most important part of the equation is representation of the periodlc sea-wave excitation. The wave excitation can be approximated by the superposition of sinusoidal waves [6].…”

Section: Ship Motion Dynamicsmentioning

confidence: 99%

“…In most of the cases, the model equations are derived from the ship motion spectral density which corresponds to a particular seaship condition, the wave excitation input, and a gaussian random noise as driving input. In other words the seastate magnitude, the ship speed, the ship heading with respect to the waves, and the disturbance pattern of the sea waves are incorporated in the model 151, [6].…”

Section: Introductionmentioning

confidence: 99%

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Neural network application to ship position estimation

Lainiotis

Plataniotis

Penon

et al.

Proceedings of OCEANS '93

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The real time estimation of ship motion is considered in this paper. The problem is viewed as an estimatiod prediction problem for partially unknown systems. A neural estimator based on a dynamic recurrent neural network is considered. The model that describes the ship motion dynamics is presented, and the neural algorithm is tested and evaluated via extensive simulations. The results show that the new algorithm has excellent performance, and a significant saving in computational time is achieved.

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A note on ship-motion prediction based on wave-excitation input estimation

Cited by 24 publications

References 4 publications

On the influence of ship motion prediction accuracy on motion planning and control of robotic manipulators on seaborne platforms

On the influence of ship motion prediction accuracy on motion planning and control of robotic manipulators on seaborne platforms

A Prediction Method for Deck Motion of Aircraft Carrier Based on Particle Swarm Optimization and Kernel Extreme Learning Machine

Neural network application to ship position estimation

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