scholarly journals Design and Implementation of Pseudo-Inverse Thrust Allocation Algorithm for Ship Dynamic Positioning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 16830-16837 ◽  
Author(s):  
Baoyu Ye ◽  
Jianbin Xiong ◽  
Qinruo Wang ◽  
Yan Luo
Keyword(s):  
Dynamic Positioning ◽  
Allocation Algorithm ◽  
Design And Implementation ◽  
Pseudo Inverse ◽  
Thrust Allocation

scholarly journals Fuel-Optimal Thrust-Allocation Algorithm Using Penalty Optimization Programing for Dynamic-Positioning-Controlled Offshore Platforms

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2128 ◽  
Author(s):  
Se Kim ◽  
Moo Kim
Keyword(s):  
Quadratic Programming ◽  
Time Domain ◽  
Dynamic Positioning ◽  
Positioning System ◽  
Time Domain Simulation ◽  
Allocation Algorithm ◽  
Dynamic Positioning System ◽  
Fully Coupled ◽  
Pseudo Inverse ◽  
Thrust Allocation

This research, a new thrust-allocation algorithm based on penalty programming is developed to minimize the fuel consumption of offshore vessels/platforms with dynamic positioning system. The role of thrust allocation is to produce thruster commands satisfying required forces and moments for position-keeping, while fulfilling mechanical constraints of the control system. The developed thrust-allocation algorithm is mathematically formulated as an optimization problem for the given objects and constraints of a dynamic positioning system. Penalty programming can solve the optimization problems that have nonlinear object functions and constraints. The developed penalty-programming thrust-allocation method is implemented in the fully-coupled vessel–riser–mooring time-domain simulation code with dynamic positioning control. Its position-keeping and fuel-saving performance is evaluated by comparing with other conventional methods, such as pseudo-inverse, quadratic-programming, and genetic-algorithm methods. In this regard, the fully-coupled time-domain simulation method is applied to a turret-moored dynamic positioning assisted FPSO (floating production storage offloading). The optimal performance of the penalty programming in minimizing fuel consumption in both 100-year and 1-year storm conditions is demonstrated compared to pseudo-inverse and quadratic-programming methods.

Fuel Optimized Thrust Allocation Algorithm Development Using Penalty-Method for the Dynamic Positioning FPSO

2016 ◽  
Author(s):  
S. W. Kim ◽  
Joseph Moo-Hyun Kim ◽  
J. W. Choi ◽  
Y. J. You
Keyword(s):  
Fuel Consumption ◽  
Penalty Method ◽  
Offshore Platform ◽  
Dynamic Positioning ◽  
Gas Emission ◽  
Allocation Algorithm ◽  
Dynamic Positioning System ◽  
Allocation Method ◽  
Pseudo Inverse ◽  
Thrust Allocation

In this paper, a thrust allocation algorithm is proposed to minimize the fuel consumption and the gas emission of the offshore platform dynamic positioning system. The thrust allocation algorithm generates thruster commands that keep the position of offshore platform while physical limitation. Generally, the offshore platform control system is an over-actuated system. Thus, a thrust allocation problem of the offshore platform can be determined as an optimization problem. In this research, a thrust allocation problem is designed to minimize the fuel-consumption and the gas emission. Fuel-optimal thrust allocation was newly formulated and solved based on penalty-method based optimization. Developed thrust allocation method was evaluated by comparing to conventional pseudo-inverse based thrust allocation. The proposed thrust allocation method was validated with comparison with an offshore support vessel static allocation cases. A fully coupled dynamics of hull, mooring, riser, and dynamic positioning system were simulated in time domain. The proposed thrust allocation method that uses penalty-method achieved a 3% accumulated fuel consumption reduction compared to the conventional pseudo-inverse method based thrust allocation algorithm in GOM 1-yr storm condition.

An Optimized Thrust Allocation Algorithm for Dynamic Positioning System Based on RBF Neural Network

2020 ◽  
Author(s):  
Ziying Tang ◽  
Lei Wang ◽  
Fan Yi ◽  
Huacheng He
Keyword(s):  
Neural Network ◽  
Power Consumption ◽  
Optimization Problem ◽  
Hydrodynamic Interaction ◽  
Rbf Neural Network ◽  
Dynamic Positioning ◽  
Positioning System ◽  
Allocation Algorithm ◽  
Dynamic Positioning System ◽  
Thrust Allocation

Abstract The thrust allocation of Dynamic Positioning System (DPS) equipped with multiple thrusters is usually formulated as an optimization problem. Hydrodynamic interaction effects such as thruster-thruster interaction results in thrust loss. This interaction is generally avoided by defining forbidden zones for some azimuth angles. However, it leads to a higher power consumption and stuck thrust angles. For the purpose of improving the traditional Forbidden Zone (FZ) method, this paper proposes an optimized thrust allocation algorithm based on Radial Basis Function (RBF) neural network and Sequential Quadratic Programming (SQP) algorithm, named RBF-SQP. The thrust coefficient is introduced to express the thrust loss which is then incorporated into the mathematical model to remove forbidden zones. Specifically, the RBF neural network is constructed to approximate the thrust efficiency function, and the SQP algorithm is selected to solve the nonlinear optimization problem. The training dataset of RBF neural network is obtained from the model test of thrust-thrust interaction. Numerical simulations for the dynamic positioning of a semi-submersible platform are conducted under typical operating conditions. The simulation results demonstrate that the demanded forces can be correctly distributed among available thrusters. Compared with the traditional methods, the proposed thrust allocation algorithm can achieve a lower power consumption. Moreover, the advantages of considering hydrodynamic interaction effects and utilizing a neural network for function fitting are also highlighted, indicating a practical application prospect of the optimized algorithm.

scholarly journals Dynamic Positioning simulations of a Thrust Allocation Algorithm considering Hydrodynamic Interactions

2018 ◽  
Vol 51 (29) ◽  
pp. 122-127 ◽  
Author(s):  
Felipe Arditti ◽  
Hans Cozijn ◽  
Ed F.G. Van Daalen ◽  
Eduardo A. Tannuri
Keyword(s):  
Hydrodynamic Interactions ◽  
Dynamic Positioning ◽  
Allocation Algorithm ◽  
Thrust Allocation
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