a survey on marine control systems

With the aid of computers and high performance software many complicated control algorithms could be applied in modelling, simulation and design of control systems for marine vehicles in

A Survey on Marine Control Systems

Tổng quan về hệ thống điều khiển hàng hải

Hung Duc Nguyen University of Tasmania / Australian Maritime College

e-Mail: nguyenhd@amc.edu.au

Abstract

In this paper, a survey is made on modelling,

simulation, control design, advances, achievements

and trends in marine control systems An overview of

history of development of marine control systems is

outlined Over a long history, many achievements on

marine control systems have been reached in both

theory and practice With the aid of computers and

high performance software many complicated control

algorithms could be applied in modelling, simulation

and design of control systems for marine vehicles

including surface vessels and underwater vehicles

The development of GNSSs (GPS, GLONASS and

GALILEO) and RTK/D-GNSSs stimulates design of

accurate, precise and high-performance control

systems for marine vehicles Telecommunication

satellite-based broadband techniques are a trend of

remote control systems at seas The paper discusses

challenging problems in design and simulation of

marine control systems The paper also deals with

some potential research projects related to the marine

control engineering at AMC/UTAS

Tóm tắt: Trong bài báo này tác giả trình bày tổng

quan về mô hình hóa, mô phỏng, thiết kế điều khiển,

những tiến bộ và thành tựu cùng các khuynh hướng

phát triển hệ thống điều khiển phương tiện trên biển

Bài báo khái quát lịch sử phát triển hệ thống điều

khiển phương tiện trên biển Qua lịch sử lâu dài cho

đến nay có nhiều thanh tựu trong hệ thống điều khiển

hàng hải Bằng sự hỗ trợ của máy tính và phần mềm

tính năng cao người ta có thể áp dụng nhiều thuật toán

điều khiển phức tạp trong mô hình hóa, mô phỏng và

thiết kế hệ thống điều khiển cho phương tiện trên

biển Sự phát triển của các hệ thống vệ tinh dẫn

đường toàn cầu (GPS, GLONASS, GALILEO) và hệ

thống định vị vệ tinh vi phân đã kích thích việc thiết

kế các hệ thống điều khiển chuẩn xác, chính xác và có

đặc tính tốt cho phương tiện trên biển Các kỹ thuật

dải băng thông rộng thông qua vệ tinh viễn thông là

một trong những khuynh hướng phát triển hệ thống

điều khiển từ xa trên biển Bài báo thảo luận về những

vấn đề thách thức trong thiết kế và mô phỏng hệ

thống điều khiển hàng hải Bài báo cũng đề cập đến

một số đề tài nghiên cứu khả thi liên quan đến lĩnh

vực công nghiệ điều khiển hàng hải tại AMC/UTAS

Nomenclature

Symbol Unit Meaning

u, v, w, p, q, r



ν

n, e, d, , ,

   

η

Abbreviation

AMC Australian Maritime College UTAS University of Tasmania PID Proportional, Integral, Derivative LQG Linear quadratic Gaussian GPS Global Positioning System GNSS Global Navigation Satellite Systems

DP Dynamic positioning D-GPS Differential GPS RTK-GPS Real-time Kinematic-GPS IFAC International Federation of Automatic

Control ECEF Earth-centred Earth-fixed frame ECI Earth-centred inertial frame NED North-East-Down frame FPP Fixed pitch propeller CPP Controllable pitch propeller

1 Introduction

Marine control engineering is about applications of control theories into marine and offshore systems It involves the research and development of new control algorithms, hardware and software for control systems

in maritime engineering systems

Marine transport is more cost-effective than other transports The world’s fleets carry the majority of cargo In many countries like EU, Australia, America, Japan and Korea the number of seafarers is decreasing because sailing at sea is a job in severe working conditions This requires a high-level automation on board cargo carrying marine vehicles because the shipboard high-level automation can reduce the number of crew Advances in computer and information technology, data communication technique and instrumentation engineering play a very important role in development of new control solutions for optimal and high-performance control systems and fuel saving The new control solutions are based on modification of feedback control algorithm and new configuration of hardware The building of new types of marine vehicle and craft inspires new design of instrumentation and control systems

In recent decades, more and more ROVs/AUVs have been applied in exploration of seabed, discovery and

exploitation of marine resources This requires new

solutions for data communication and control

algorithms Control of ROVs/AUVs is a great

challenge because they are operating in 6-DOF

This paper is organized as follows: Section 1

Introduction; Section 2 Current status of marine

control systems; Section 3 Kinematics and kinetics;

Section 4 Overview of marine control systems;

Section 5 Modelling and identification of marine

vehicles; Section 6 Experimental facilities; Section 7

Challenges, Section 8 Trend; Section 9 Potential

projects at AMC/UTAS; and Section 10 Conclusions

2 Current Status

2.1 Overview of History

The invention of the gyroscope contributed much to

the development of a ship’s autopilot system The

development of the electronically-driven gyroscope

was motivated by the need for more reliable

navigation systems in steel ships and underwater

warfare [3][4] The successful design of the gyroscope

at the beginning of 20th century was the key

breakthrough in automatic ship control since it led to

the development of autopilots and other control

systems (see Fig 1)

Fig 1 Diagram of history of marine control systems

2.2 Research Activities

The IFAC organizes every 3 year (triennial)

conferences on marine systems including CAMS

(Control Applications in Marine Systems), MCMC

(Manoeuvring and Control of Marine Crafts) The

scopes of these IFAC conferences on marine control

systems are broad ranges from autopilot to dynamic

positioning systems and various applications of

control theories in control, simulation and modelling

of marine vehicles These IFAC conferences on control of marine vehicles cover a wide range of scopes, for example, ship manoeuvring, autopilots, roll damping, dynamic positioning, automatic mooring and anchoring, navigation, guidance and control of autonomous surface and underwater vehicles, operational safety etc

2.3 Development of GPS/GNSS and IMU/INS

Since 1995 when the GPS became operational for civil use, the accuracy of GPS/GNSS has been improved significantly The augmentation, integration and availability of GPS, GLONASS and GALILEO for civil use with high accuracy, precision and reliability inspire engineers and researchers to design new types of tracking and path-following control system Moreover, the development of IMU/INS and integration of GNSS and IMU/INS allows more accurate and precise navigation systems to be designed and helps more complicated marine control systems to be developed

3 Kinematics and Kinetics of Marine

Vehicles

3.1 Reference Frames

In the design of marine control systems, some reference frames for descriptions of kinematics and

kinetics of marine vehicles are often used Fig 2

shows centred reference frames (the

Earth-centred Ear-fixed frame xeyeze, and the Earth-centred inertial frame xiyizi), and geographic reference frames

(the North-East-Down coordinate system xnynzn and the body-fixed reference frame xbybzb) [3][4]

Fig 2 The ECEF frame x e y e z e is rotating with angular rate with respect to an ECI frame x i y i z i fixed in the space [3][4]

Fig 3 shows the 6DOF velocities in the body-fixed frame Table 1 gives the notation for the 6DOF

motions, forces and moments, linear and angular velocities, position and Euler angles for marine vehicles

zi, ze

ωe

yn

xn

zn

BODY

y

x

z

NED

ECEF

ωet

ye

xe

yi

xi

Fig 3 The 6DOF velocities u, v, w, p, q and r in the

body-fixed reference frame xbybzb [3][4]

Table 1 The notation of SNAME (1950) for marine

vessels

3.2 Equations of Kinematics

Referring to Fig 2 the 6-DOF kinematic equations in

the NED (north-east-down) reference frame in the

vector form are,

 





where

  nb  3 3 

3 3



J η

with η 3S3 and ν 3 The angle rotation

matrix n  3 3

b





R Θ is defined in terms of the

principal rotations,

x,



   

c 0 s

s 0 c



  

z,



  

where s= sin(.), c= cos(.) using the zyx-convention,

 

n

b : z, y, x,

or

 

n b

c c s c c s s s s c c s

s c c c s s s c s s s c

The inverse transformation satisfies,

 1  



  

The Euler angle attitude transformation matrix is:

  10 s tc c ts

0 s / c c / c



   

 

1

0 c c s

0 s c c





 

   

    

T Θ   90o (7)

It should be noted that T Θ is undefined for a pitch angle of o

90

   and that  1  T 

T Θ T Θ

3.3 Equations of Kinetics Referring to Fig 3 the 6-DOF kinetic equations in the

body-fixed reference frame in the vector form are,

      0 wind wave

where

M = MRB+MA: system inertia matrix (including added mass);

 

C ν = CRB ν CA ν : Coriolis-centripetal matrix

(including added mass);

 

D ν : damping matrix;

 

g η : vector of gravitational/buoyancy forces and

moments;

0

g : vector used for pretrimming (ballast control);

τ: vector of control inputs;

wind

τ : vector of wind-induced forces and moments; and

wave

τ : vector of wave-induced forces and moments

3.4 Equations for Manoeuvring of Surface Vessels

For surface vessels their motions are often limited to 4-DOF: surge, sway, yaw and roll It is assumed that the vessel is symmetric about the plane of XGZ and the origin and the mass concentration at the centre of gravity, four 4-DOF kinetic equations are expressed

as [13],

zz

xx

where

m is the mass of the vessel;

Izz is the moment of inertia about z-axis; and

Ixx is the moment of inertia about x-axis

X, Y, N and K are forces and moments acting on the vessel, including propeller-generated forces and moments, hydrodynamic forces and moments due to interaction between the propeller and the hull, rudder-

or control surface-induced forces and moments and

external disturbances

Equation (1) is simplified as,

pos

pos

y u sin v cos (13)

3.5 Equations for Environmental Disturbances

Environmental disturbances include wind, waves and

currents According to Fossen [3] for control system

design it is common to assume the principle of

superposition when considering wind and wave

disturbances With effects of external disturbances

Equation (8) is rewritten as,

 

0

M ν C ν ν M ν C ν ν D ν ν

where wτwindτwave and νr  ν νc (where

6

c

ν is the velocity of the ocean current expressed

in the NED) Further information on modeling

environmental disturbances can be found in [2][3][4]

3.6 Discrete-time Models for Marine Vehicles

The classical methods of designing control systems

are using continuous-time models including

differential equations, transfer functions and

state-space models The computer-aided methods are using

discrete-time models, including difference equations,

pulse transfer functions and discrete-time state space

models Auto-regressive models are often used for

stochastic control algorithms and model reference

control Discretisation of the following

continuous-time state-space model

results in

(15)

or

(16) where

(17) (18) For stochastic control systems the following

auto-regressive average moving exogenous model and

auto-regressive exogenous model are used:

(19) (20)

4 Overview of Marine Control Systems –

Motion Control

Motion control of marine vehicles involves the

guidance, navigation and control of:

 surface vessels;

 underwater vehicles including submersibles

and submarines; and

 oil rigs, floating and subsea structures

The motion control systems for marine vehicles

include ship autopilots, roll damping/stabilising

systems and dynamic positioning systems

For surface vessels the desired motions are surge,

sway and yaw (turning) while undesired motions are

heave, roll and heel, pitch and trim Surge, sway and yaw motions are often controlled by a rudder or control surface, FPP or CPP, side thrusters The undesired motions are reduced to an acceptable level

by some motion control strategies such as fins, trimtabs, interceptors, T-foils, rudder-roll, lifting foil and air cushion support

4.1 Guidance, Navigation and Control of Marine Vehicles

An entire modern control system for marine vehicles

has three subsystems as shown in Fig 4 [3]:

 guidance system;

 sensor and navigation system; and

 control system

Fig 4 The GNC signal flow [3]

The guidance system is used to generate desired signals based on the prior information, predefined trajectory and weather data from weather forecast stations Some techniques that are applied in the guidance systems are target tracking, trajectory tracking, path following for straight-line paths, and path following for curved paths [3]

The sensor and navigation system consists of necessary sensor and navigation devices such as GPS/GNSS receivers, wind gauges, depth sounder, speed log, IMU/INS and engine sensors In order to have “clean” data for control purposes observer, filter and estimator techniques are applied

The control system is where control algorithms are synthesised and control signals are computed Modern control algorithms are applied

Fig 5 shows an example of recursive optimal

trajectory control system

Fig 5 The GNC signal flow of the recursive optimal

trajectory tracking control system [7]

kh

(k 1)   exp h k   exp k 1 h    k d 

x Ax Bu

k 1   k   k

 

exp h



1



Δ A Φ I B

 1    1    1  

z k  z k  z k

 1    1    

z k  z k  k

As shown in Fig 5 the control system consists of a

guidance system that generates desired course, speed

and course changing points based on the LOS,

waypoint and decay exponential techniques The

sensor and navigation consists of GPS/IMU/INS,

gyrocompass, sensors and a recursive estimator The

control system consists of a controller based on the

optimal control law

4.2 Autopilots

Autopilots are used for course keeping and changing

The common method for conventional vessels

equipped with a propeller and rudder is illustrated in

Fig 6 As shown in Fig 6 the course (yaw) angle and

yaw rate are measured by a compass and gyro For a

waterjet-propelled vessel, the course is controlled by

the waterjet nozzle

Fig 6 Ship’s autopilot system [4]

Modern and intelligent control algorithms have been

applied in the autopilots Fig 7 shows an example of

a stochastic model based autopilot with a combination

of a recursive estimation algorithm and the self-tuning

control algorithm Fig 8 shows an example of the

neural networks-based autopilot

Fig 7 Ship’s recursive self-tuning autopilot system

Fig 8 Ship’s neural networks-based autopilot system

4.3 Rudder-roll Stabilisation Systems

The roll motion of a marine vehicle has bad and

unexpected effects on crew and passenger heath and

cargo as well as the stability of the vehicle The

effects of roll motion (especially the parametric roll motion) are seasickness, damage of cargo and damage

of vessel A rudder-roll reduction system is based on

the principle illustrated in Fig 9 and Fig 10 The

main requirements for this system are:

 fast rudder slew rate;

 accurate measurement of roll motion; and

 low pass filters

Fig 9 Principle of a rudder-roll stabilisation system

Fig 10 Autopilot system with rudder-roll reduction

Fig 11 shows an example of responses of an autopilot

system with rudder-roll damping function

Fig 11 Responses of an autopilot system with rudder-roll

reduction

4.4 Dynamic Positioning Systems

Dynamic positioning systems are used to control marine vehicles at very low speeds where the effect of rudder or control surface is almost zero A modern DPS has many functions such as autopilot, dynamic positioning, trajectory tracking and shifting anchor alarm To design a DPS the waypoint, LOS and decay

exponential techniques are applied Fig 12 shows the

main forces and moments generated by actuators and

external disturbances on a vessel equipped with a

DPS

Fig 12 The main forces and moments for DPS design

(courtesy of Kongsberg)

In the DPSs there are more than two controls DPSs

require network data communication buses Modern

and intelligent control algorithms such as optimal

control, self-tuning control and fuzzy logic control

have been applied in the design of DPSs

4.5 Networked Control Systems and Integrated

Bridge

Nowadays marine control systems are in forms of a

networked control system, distributed control system

and integrated bridge that allow the operator to

control many onboard systems The networked

control systems have data communication buses such

as NEMA, CANOpen, and Profibus Fig 13 shows a

networked control system with NAMA data

communication devices

Fig 13 Concept of networked control system with data

communication bus (NEMA)

The centralised control systems are obsolete and

replaced with distributed and networked control

systems For high-level automation marine vehicles a

networked control system has some main features:

integrated, distributed, supervisory, redundancy and

safety as shown in Fig 14

Fig 14 Example of high-level automation control system on

a modern vessel (courtesy of Kongsberg)

4.6 Control Systems for ROVs/AUVs, Oil Rigs and Floating Structures

Control of ROVs/AUVs, oil rigs and floating structures is a greater challenge in comparison with control of surface vehicles because of their complexity, moving at low speeds and underactuation

Control algorithms and methods for ROVs/AUVs are described in [3][4][11] and [12]

5 Manoeuvrability, Modeling and System Identification of Marine Vehicles (Hydrodynamics)

To assess manoeuvrability of marine vehicles is important for safe operation The manoeuvrability of ocean vehicles must meet IMO standards, including interim standards for ship manoeuvrability IMO Resolution A.751(18), 1993 and standards for ship manoeuvrability IMO Resolution MSC137(76), 2002, issued by the IMO Maritime Safety Committee The marine vehicles built with very poor manoeuvring qualities will cause marine casualties and pollution The manoeuvrability is often related to the:

 seakeeping: a measure of how well-suited a marine vehicle is to conditions when underway; and

 seaworthiness: the ability of a marine vehicle

to operate effectively under severe sea conditions, i.e very good seakeeping ability

To quantify the manoeuvrability is to identify hydrodynamic coefficients of the manoeuvring models Its applications are:

 manoeuvring characteristics (for various manoeuvres);

 stability assessment;

 computer and HIL simulation (full mission

manoeuvring simulators) for educational and

training purposes;

 control design (stochastic control, model based

adaptive control);

 fault detection and diagnostics; and

 prediction of forces and moments due to the

interaction between many submersible bodies

The quantitative representation of manoeuvring

characteristics of marine vehicles consists of

straight-line stability and directional stability The methods to

assess the manoeuvring characteristics are the turning

circle test, Kempf’s zig-zag test, Dieudome’s pull-out

manoeuvre test, Bech’s reverse spiral manoeuve test

and stopping trial

Many authors proposed manoeuvring mathematical

models, for examples, Abkowitz (USA: SNAME),

MMG model group in Japan (SNAJ, JTTC), Norrbin

(1970), Blanke (1981), Nomoto and Sons, etc Further

information can be found in [3][4][5]

The most common and well-known model of

manoeuvring is the Nomotor’s first order model that

relates the rudder angle and yaw rate (turning rate):

Tr  r K (21)

where T and K are manoeuvrability indices

In order to quantify the manoeuvring characteristics

of marine vehicles and determine hydrodynamic

coefficients of the manoeuvring mathematical models,

it is necessary to conduct full-scaled or model-scaled

experiments as shown in Fig 15

Fig 15 Experiments for prediction of hydrodynamic

coefficients

In order to estimate hydrodynamic coefficients of a

vehicle there are several methods among which the

following are widely used:

 Recursive least squares algorithm; and

 Recursive prediction error method

5.1 Recursive Least Squares Algorithm (RLSA)

The recursive least squares algorithm is based on the

least squares algorithm proposed by Gauss This

method is illustrated by the flowchart in Fig 16

Fig 16 Flowchart of RLSA

5.2 Recursive Prediction Error Method (RPEM)

The recursive prediction error algorithm was proposed by Ljung based on the Kalman filter and is

illustrated by flowchart in Fig 17

Fig.17 Flowchart of RPEM

4.7 Fault Detection and Diagnosis Monitoring and Supervision and Fault Tolerant Control

Recursive system identification methods are applied

in fault detection and diagnostic monitoring and supervision of marine and offshore engineering systems They are also applied in fault-tolerant control The conceptual system of fault detection and diagnostic monitoring and supervision is shown in

Fig 18 The fault detection system requires prior

knowledge of the plant (theoretical data) and sensors

to collect actual data The system compares actual data with the theoretical data and thus detects any

faults occurring in every component of the

engineering systems when there is a great difference

between two sets of data The system provides

solutions to manage faults Further information on

fault detection and diagnostic monitoring and

supervision can be found in [14] [15]

Fig.18 Concept of fault detection and diagnostic monitoring

and supervision for marine and offshore systems

6 Experimental Facilities

In order to support control design and to realise

marine control systems it is necessary to utilise

experimental facilities for full-scaled and

model-scaled experiments Experiments require the

following facilities:

 physical models or prototypes of marine

vehicles;

 model test basin with artificial wavemaker and

wind generators for free-running models;

 towing tank with PMM for captive models;

 full-scale vessels (expensive); and

 control hardware (instrumentation electronics,

data communication) and software

The AMC/UTAS possesses the world’s leading

maritime experimental facilities The facilities include

the towing tank (see Fig 19 and Fig 20), model test

basin (see Fig 21) cavitation tunnel (see Fig 21), and

circulating water channel (see Fig 22), full mission

ship manoeuvring simulator, dynamic positioning

simulator, and training vessel (Bluefin)

Fig.19 AMC Towing Tank

Fig.20 AMC Towing Tank with PMM and captive model

Fig.21 AMC Model Test Basin with wavemakers and models

Fig.21 Three dimensional view of the AMC Capvitation

Tunnel

Fig 22 The CWC and its arrangement

Other institutes that also have the world’s leading maritime experimental facilities are Norwegian University of Science and Technology and MARINTEK, Tokyo University of Marine Science and Technology

7 Challenging Problems

In design and simulation of marine control systems some challenging problems are:

 underwater communication between the AUVs and mother vessel;

 energy for ROVs/ AUVs that operate underwater for a long time;

 fault detection and diagnostics and safety, this leads to losses of expensive ROVs/AUVs

 control and operation of ROVs/AUVs at very deep waters;

 watertight electronic components; and

 in-door navigation techniques for experiments

8 Future Trend

Recent trends show the following applications:

 networked control systems with data

communication buses;

 Internet-based control systems utilising

satellite broadband services;

 applications of advanced and intelligent

control algorithms;

 wireless network;

 underwater acoustic navigation systems for

ROVs/AUVs; and

 optical communication between ROVs/AUVs

and the carriage vessels

Fig 23 shows an example of remote control system

via satellite broadband services in Norwary Fig 24

shows another example of remote control system via

satellite broadband services in Japan

Fig 23 Remote control system via satellite broadband

services (Norway)

Fig 23 Remote control system via satellite broadband

services at Tokyo University of Marine Science and

Technology, Japan

9 Potential Projects Related to Marine

Control Engineering at AMC/UTAS

The AMC, possessing the world’s leading maritime

experimental facilities, is undergoing several potential

projects related to marine control engineering These

projects are:

 design and testing of ROV/AUVs;

 modelling, simulation and control of

ROVs/AUVs;

 modelling, simulation and control of AUVs using a cyclic and collective pitch propeller;

 modelling and control of surface vessels with electrically-operated water-jet (GreenLiner)

 development of ROVs/AUVs with a collective and cyclic pitch propeller;

 development of a (solar-wind-diesel) trybrid trimaran and its control systems;

 development of automatic manoeuvring systems for surface vessels;

 development of dynamic positioning systems

by applying advanced control algorithms; and

 prediction, simulation of hydrodynamic interaction between many submersible bodies

10 Conclusions

The paper has discussed the current status of marine control systems and description of kinematics and kinetics of marine vehicles for design and analysis of their control systems It has overviewed marine control systems and modelling and identification of marine vehicles To design and analyse control systems full-scaled and model-scaled experiments are necessary and require maritime engineering specialised experimental facilities such model test basin, towing tank, circulating water channel The paper has also dealt with future trend of marine control application and some potential projects at AMC/UTAS

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Ship Manoeuvring Characteristics (in Japanese:

小林英一・影本浩・古川芳孝:第2章操縦運

動の数学モデル運動性能研究委員会・第12

回シンポジウム。), Japan, 1995

[14] Isermann, R Fault-Diagnosis Systems – An

Introduction from Fault Detection to Fault

Tolerance Springer, 2006

[15] Blanke, M., Kinnaert, M., Lunze, J and

Staroswiecki, M Diagnosis and Fault-Tolerant

Control, 2nd Edition Springer, 2006

Biography

Dr Hung Nguyen is a lecturer

in Marine Control Engineering

at National Centre for Maritime Engineering and Hydrodynamics, Australian Maritime College, Australia

He obtained his BE degree in Nautical Science at Vietnam Maritime University in 1991, then he worked as a lecturer there until 1995 He completed the MSc in Marine Systems Engineering in

1998 at Tokyo University of Marine Science and

Technology and then the PhD degree in Marine

Control Engineering at the same university in 2001

During April 2001 to July 2002 he worked as a

research and development engineer at Fieldtech Co

Ltd., a civil engineering related nuclear instrument

manufacturing company, in Japan He moved to the

Australian Maritime College, Australia in August

2002 His research interests include guidance,

navigation and control of marine vehicles, self-tuning

and optimal control, recursive system identification,

real-time control and hardware-in-the-loop simulation

of marine vehicles and dynamics of marine vehicles

Appendix Nonlinear Mathematical Models of Marine Vehicles for Control

Design and Simulation

Nonlinear mathematical models for design and analysis of marine control systems are as follows:

 Model of Cargo Mariner Class;

 Model of Training Vessel Shoji Maru;

 Model of Container Vessel;

 Model of Tanker Esso; and

 Models of Underwater Vehicles

These nonlinear mathematical models are provided upon request