Review of Dynamic Positioning Control in Maritime Microgrid Systems
Abstract
:1. Introduction
2. Marine Vessel DPS Description
2.1. Overview of DPS Applications
2.1.1. Power Subsystem
2.1.2. Signal Processing Subsystem
2.1.3. Sensors Subsystem
 CPU Processor/joystick systems console
 Instruments sensor units, etc.
 Station reference unit consists of:
 
 Navigation system
 
 Acoustic systems
 
 Microwave and laser systems
 
 The interface control unit, which is used as an interface to read the thrusters, switchboard feedback signals, as well as outputting command signals from the DP system to external units.
 Observing system and operator control panels.
2.1.4. Thruster Subsystem
2.1.5. Power Management Subsystem
2.2. Dynamic Positioning Vessel Classification
 DP class 1 is not redundant and can be positioned for a particular fault.
 DP class 2 is redundant to facilitate no particular fault in the operational condition lead to system failure. Therefore, loss of vessel position does not occur due to a particular fault on the power generations, distributions, and automatic valves, etc. However, it possibly failure will happen for example in cables, pipes, manual valves as a static system.
 In vessels with DP class 3, flooding or firing occurs must also be removed in a nonsystem enclosure. A loss of position should not result in a sudden defeat, including the entire distillation section of the fire or the dewatering chamber.
2.3. Dynamic Positioning Mathematical Model
2.4. Dynamic Positioning Control Principles
3. Review of Dynamic Positioning Controls
3.1. Expanded Kalman Filter (EKF)
3.2. Model Predictive Control Model
 For measuring the average of environmental forces induced by the sea disturbances, the environment compensator is used to maintain the necessary position under averaged conditions.
 Predicting the current position of the ship movement as input for the MPC control. While the operational restrictions are predicted to be overcome, the controller responds to guarantee that the ship stays within the functional area. For the nonlinear predictor controller, a model is an online optimization feature that finds the best possible mix between the use of thruster and the prediction of passing through operational constraints. The position predictor includes the ship motion mathematical model used in the DP Kalman filter.
3.3. Fuzzy Logic Control Method
3.3.1. Preprocessing
 Adjusting the gradation error function as integers.
 Normalizing or scrambling to a specific standard collection.
 Clarifying due to the Removal of the noise of environmental sensors.
 Combining several measurements to achieve main indicators, variation, and integration.
3.3.2. Fuzzification and Defuzzification
3.4. Fuzzy Adaptive Control (FAC)
3.5. Neural Network Control Method
3.6. NeuroFuzzy Control Method
 To overcome the computational difficulties of the conservative BPAL method, the derivatives of the virtual control signals are found through the dynamic surface control.
 The proposed designed controller could be easily employed in practical applications with no requirement to apply the neural network and state approximations to collect model parameters.
 The prediction errors were combined with position signal errors to organize the neural network updating laws, which improves the neuron weight adjustment and tracking performance.
3.7. Adaptive Sliding Mode
4. Conclusions and Future Research
Author Contributions
Funding
Conflicts of Interest
Appendix A
Mathematical Models  Formula  Description 

Three degrees of freedom (3DOF), Low frequency (LF) of vessel movements [22,23]  $M\dot{v}=Dv+{\tau}_{thr\text{}}+{\tau}_{env}$ $M=\left[\begin{array}{ccc}m{X}_{\dot{u}}& 0& 0\\ 0& m{Y}_{\dot{v}}& m{x}_{g}{Y}_{\dot{r}}\\ 0& m{x}_{g}{N}_{\dot{v}}& {I}_{z}{N}_{\dot{r}}\end{array}\right]$ $D=\left[\begin{array}{ccc}{X}_{u}& 0& 0\\ 0& {Y}_{v}& {Y}_{r}\\ 0& {N}_{v}& {N}_{r}\end{array}\right]$ ${\tau}_{env}={[{\tau}_{{X}_{env}}\text{}{\tau}_{{Y}_{env}}\text{}{\tau}_{{N}_{env}}]}^{T}$  ship velocity vector $v={[u\text{}v\text{}r]}^{T}$ position and orientation $\eta ={[x\text{}y\text{}\psi ]}^{T}$ where M is the inertial system matrix with the assumption that my plane includes mass distribution. The linear damping matrix is introduced with matrix D for LF application. The relation between $v$ and $\eta $ is given by 3DOF with principle rotation matrix about the zaxis, $\dot{\eta}=R(\psi )v$ where $\psi $ is the swaying angle.
$$R(\psi )=\left[\begin{array}{ccc}\mathrm{cos}(\psi )& \mathrm{sin}(\psi )& 0\\ \mathrm{sin}(\psi )& \mathrm{cos}(\psi )& 0\\ 0& 0& 1\end{array}\right]$$

Environmental Disturbances  $h(s)=\frac{2{K}_{i}\lambda {\omega}_{0}\delta s}{{s}^{2}+2\lambda {\omega}_{0}+{\omega}_{0}{}^{2}}$  The transfer function $h$ is used for simulating the wave forces and moments and passing band limit in a surface vessel in 3DOF. This Model was introduced by Saelid [6], where he proposed the term $\lambda $ as a damping term to develop the performance of the PiersonMoskowitz spectrum. Beside KI is again for i = 1, 2, 3, expressive X, Y, and N. Three different coefficients $K{x}_{w}\text{},K{y}_{w,}K{n}_{w}$ are used For generating forces (${\tau}_{{X}_{env}}\text{}{\tau}_{{Y}_{env}}$) and moment (${\tau}_{{N}_{env}}$) 
Network Functions  Formula  Comments 

Linear  $x={\displaystyle {\displaystyle \sum}_{j=1}^{N}}wjyj+\mathsf{\theta}$  frequently is used for NN activation function 
Higherorder exhibited  $x={\displaystyle {\displaystyle \sum}_{j=1}^{\mathrm{N}}}{{\displaystyle \sum}}^{\text{}}wjkyjyk+\mathsf{\theta}$  x is a weighted as linear terms of the input variable. 
Delta  $x={\displaystyle {\displaystyle \prod}_{j=1}^{N}}wjyj+\mathsf{\theta}$  Seldom used 
Activation Functions  Formula a = f(x)  Derivatives  Comments 

Sigmoid  $f\left(x\right)=\frac{1}{1+{e}^{x/t}}$  $f\left(x\right)\left[1f\left(x\right)\right]/T$  Frequently uses and derivative of x as f(x) can be computed directly. 
Hyperbolic tangent  $f\left(x\right)=\mathrm{tan}h\left(\frac{x}{T}\right)$  $(1\left[f{(x)}^{2}\right])/T$  T = temperature parameter 
Inverse tangent  $f\left(x\right)=\frac{2}{\pi}{\mathrm{tan}}^{1}\left(\frac{x}{T}\right)$  $\frac{2}{\pi T}\xb7\frac{1}{1+{\left(\frac{x}{t}\right)}^{2}}$  Less frequently used 
Linear  $f\left(x\right)=ax+b$  a  Most commonly used 
Control Models  Advantages  Disadvantages 

Model predictive control include Extended Kalman filters [30,31,32,33,34,35] 


NeuroPD controller [21] 


Adaptive neural networks controller [22,89,90] 


NeuroFuzzy controller [21,23] 


Fuzzy controller [62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79] 


Adaptive fuzzy controller (AFC) [15,16,17,18,19,20] 


PID controller [2,24,25,26,27,28,29] 


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Classification  IMO  LR  DNV  ABS  

Description  
No redundancy exists, the vessel is provided by the manually stationary keeping and automatically heading control under the sea disturbances.  Class 0  DP(M)  DNVT  DPS0  
No redundancy exists, only one computer system for the DPS is equipped to automatically control the deviation of the vessel station and heading displacement.  Class 1  DP(A)  DNVAUT DNVAUTS  DPS1  
Two redundant computer systems are used to automatically control the station and heading movement under the sea disturbances. Hence, the vessel position will not lose due to the failure of the dynamic system.  Class 2  DP(2A)  DNVAUTR  DPS2  
Three redundant computer systems are employed to control heading and environmental disturbances, during the DPS failure containing the loss of the unit due to overflow or fire situations.  Class 3  DP(3A)  DNVAUTRO  DPS3 
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Mehrzadi, M.; Terriche, Y.; Su, C.L.; Othman, M.B.; Vasquez, J.C.; Guerrero, J.M. Review of Dynamic Positioning Control in Maritime Microgrid Systems. Energies 2020, 13, 3188. https://doi.org/10.3390/en13123188
Mehrzadi M, Terriche Y, Su CL, Othman MB, Vasquez JC, Guerrero JM. Review of Dynamic Positioning Control in Maritime Microgrid Systems. Energies. 2020; 13(12):3188. https://doi.org/10.3390/en13123188
Chicago/Turabian StyleMehrzadi, Mojtaba, Yacine Terriche, ChunLien Su, Muzaidi Bin Othman, Juan C. Vasquez, and Josep M. Guerrero. 2020. "Review of Dynamic Positioning Control in Maritime Microgrid Systems" Energies 13, no. 12: 3188. https://doi.org/10.3390/en13123188