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

2021 ◽  
Author(s):  
Ziying Tang ◽  
Lei Wang ◽  
Fan Yi ◽  
Huacheng He
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Dynamic Positioning ◽  
Positioning System ◽  
Allocation Algorithm ◽  
Dynamic Positioning System ◽  
Thrust Allocation

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 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.

Time-Domain Dynamic Analysis for Dynamic Positioning System of Deepwater Semi-Submersible

2012 ◽  
Vol 204-208 ◽  
pp. 4518-4522 ◽  
Author(s):  
Li Ping Sun ◽  
Shu Long Cai ◽  
Jing Chen
Keyword(s):  
Dynamic Analysis ◽  
Deep Water ◽  
Time Domain ◽  
Pid Control ◽  
Oil And Gas ◽  
Dynamic Positioning ◽  
Positioning System ◽  
Dynamic Positioning System ◽  
The Time Domain ◽  
Thrust Allocation

Semi-submersible plays an important role in ocean oil and gas exploitation. This paper carried out some researches for the dynamic positioning system (DPS) of a deep water semi- submersible. Mathematic modal was made, and a special program was created with M-language for the time-domain dynamic analysis of the dynamic positioning system of the deep water semi-submersible, on basis of the mathematic modal. PID control strategy, kalman filtering theory and optimal thrust allocation method were used in the analysis. Simulation result indicated the DPS of this platform is safe and efficient.

Optimal Thrust Allocation in a Dynamic Positioning System

2008 ◽  
Author(s):  
J. A. Leavitt
Keyword(s):  
Dynamic Positioning ◽  
Positioning System ◽  
Positioning Systems ◽  
Dynamic Positioning System ◽  
Surface Vessels ◽  
Thrust Allocation

An existing approach to optimizing thrust allocation in surface vessels is considered for general use with dynamic positioning systems. A solution to the power limiting problem is presented, and the handling of azimuthing thrusters is significantly improved. Various other considerations related to thrust allocation are treated. A generalized algorithm is developed.

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