Neural network based fin control for ship roll stabilization with guaranteed robustness

Luo, Weilin; Hu, Bingbing; Li, Tieshan

doi:10.1016/j.neucom.2016.12.023

Cited by 25 publications

(12 citation statements)

References 26 publications

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“…scale matrix with the range of n, which is explained in accordance with the information in[9]. Moreover, they have copied the disrupting environmental impacts by including secon d-order waves, ocean currents, and wind.With regard to the optimum position and orientation of the ship First, the PID control scheme is studied for the simulation.…”

supporting

confidence: 63%

“…Artificial NN are patterns of information processing created using the imitating biological NN like that of the person brain. The artificial neural network is a data processing system which has been inspired by human brain and has entrusted the processing of data to small, very large processors that work in a networked and parallel manner to solve a problem [9]. Generally, theses networks can be summarized as three main parts:…”

Section: Neural Networkmentioning

confidence: 99%

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Dynamic modeling and adaptive controlling in GPS-intelligent buoy (GIB) systems based on neural-fuzzy networks

et al. 2020

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Recently, various relations and criteria have been presented to establish a proper relationship between control systems and control Global Positioning System (GPS)-intelligent buoy system. Given the importance of controlling the position of buoys and the construction of intelligent systems, in this paper, dynamic system modeling is applied to position marine buoys through the improved neural network with a backstepping technique. This study aims at developing a novel controller based on adaptive fuzzy neural network to optimally track the dynamically positioned vehicle on water with unavailable velocities and unidentified control parameters. In order to model the network with the proposed technique, uncertainties and the unwanted disturbances are studied in the neural network. The presented study aims at developing a neural controlling which applies the vectorial back-stepping technique to the surface ships, which have been dynamically positioned with undetermined disturbances and ambivalences. Moreover, the objective function is to minimize the output error for the neural network (NN) based on closed-loop system. The most important feature of the proposed model for the positioning buoys is its independence from comparative knowledge or information on the dynamics and the unwanted disturbances of ships.The numerical and obtained consequences demonstrate that the controller system can adjust the routes and the position of the buoys to the desired objective with relatively few position errors.

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supporting

confidence: 63%

Section: Neural Networkmentioning

confidence: 99%

Dynamic modeling and adaptive controlling in GPS-intelligent buoy (GIB) systems based on neural-fuzzy networks

et al. 2020

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supporting

confidence: 63%

Dynamic Modeling and Adaptive Controlling in GPS-Intelligent Buoy (GIB) Systems Based on Neural-Fuzzy Networks

Zhang

Ashraf

Liu

et al. 2020

Preprint

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Recently, various relations and criteria have been presented to establish a proper relationship between control systems and control Global Positioning System (GPS)-intelligent buoy system.Given the importance of controlling the position of buoys and the construction of intelligent systems, in this paper, dynamic system modeling is applied to position marine buoys through the improved neural network with a backstepping technique. This study aims at developing a novel controller based on adaptive fuzzy neural network to optimally track the dynamically positioned vehicle on water with unavailable velocities and unidentified control parameters. In order to model the network with the proposed technique, uncertainties and the unwanted disturbances are studied in the neural network. The presented study aims at developing a neural controlling which applies Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 8 April 2020 the vectorial back-stepping technique to the surface ships, which have been dynamically positioned with undetermined disturbances and ambivalences. Moreover, the objective function is to minimize the output error for the neural network (NN) based on closed-loop system. The most important feature of the proposed model for the positioning buoys is its independence from comparative knowledge or information on the dynamics and the unwanted disturbances of ships.The numerical and obtained consequences demonstrate that the controller system can adjust the routes and the position of the buoys to the desired objective with relatively few position errors.

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“…Reference [16] used a linear quadratic regulator for wave piercing catamarans motion control, and reference [17] developed an intelligent control based on PSO (Particle swarm optimization) for a ship roll motion. The authors of [18] presented an adaptive robust fin controller based on a feedforward neural network. These advanced methods have excellent results in simulation, however, these algorithms perform poorly in actual engineering.…”

Section: Introductionmentioning

confidence: 99%

A Method of a Trimaran Vertical Movements Reduction Control and Hardware Realization

2019

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The vertical movements of a trimaran, which can produce a vertical acceleration, always make people feel seasickness and cause equipment damage on the board. This paper presents a method of a trimaran vertical stabilization control. First, the trimaran mathematical equation is obtained by the numerical simulation and verified by the trimaran experiment in the water tank. Then, the vertical stabilization appendages, including T-foil and flap, are designed and installed at the bottom of the trimaran. Second, an active controller of the appendages is designed and a decoupling method is applied to decouple T-foil and flap, the attack angles of T-foil and flap are obtained in real-time, and the mathematical simulation verified the effect of the decoupling method. Finally, every hardware parts are selected and composed, then, the hardware of the control system is realized and installed on the trimaran, in water tank experiment, the controller was verified in water tank experiment and the experiment results show that it has a good vertical stabilization effect for the trimaran.

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Neural network based fin control for ship roll stabilization with guaranteed robustness

Cited by 25 publications

References 26 publications

Dynamic modeling and adaptive controlling in GPS-intelligent buoy (GIB) systems based on neural-fuzzy networks

Dynamic modeling and adaptive controlling in GPS-intelligent buoy (GIB) systems based on neural-fuzzy networks

Dynamic Modeling and Adaptive Controlling in GPS-Intelligent Buoy (GIB) Systems Based on Neural-Fuzzy Networks

A Method of a Trimaran Vertical Movements Reduction Control and Hardware Realization

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