Hội nghị toàn quốc về Điều khiển và Tự động hoá - VCCA-2011

VCCA-2011
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: [email protected] 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

ν 
T
u,v,w,p,q,rν

η 
T
n,e,d, , ,   ηAbbreviation
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

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
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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

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 Earth-centred reference frames (the Earth-
centred Ear-fixed frame x

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.
z
i
, z
e

ω
e

y
n

VCCA-2011 Fig. 3 The 6DOF velocities u, v, w, p, q and r in the body-
fixed reference frame x
b
y
b
z
b
[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,
 

η J η ν
(1)
where
 
 
 
n
b 3 3

0 s c



   




R
,
y,
c 0 s
0 1 0
s 0 c







  

R
and
z,
c s 0
s c 0
0 0 1



              


     

R Θ
(5)
The inverse transformation satisfies,
   
1
n b T T T
b n x, y, z,

  
R Θ R Θ R R R
(6)
The Euler angle attitude transformation matrix is:
 
1 s t c t
0 c s
0 s /c c / c

   


   



pitch angle of
o
90  
and that
   
1T

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
      Mν C ν ν D ν ν g η g τ τ τ
(8)
where
M = M
RB
+M
A
: system inertia matrix (including added
mass);
 
C ν
=
   
RB A
C ν C ν

(9)
mu mvr X
(10)
zz
I r N
(11)
xx
IK
(12)
where
m is the mass of the vessel;
I
zz
is the moment of inertia about z-axis; and
I
xx
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-
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or control surface-induced forces and moments and
external disturbances.
Equation (1) is simplified as,
pos
x ucos vsin   

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
(14)
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)


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]
   
 
 
 
 
k 1 h


A y B u e
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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


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
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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

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

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.


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).


 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.
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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.

 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.

References
[1] Roberts, G.N. and Sutton, R (Editors).
Advances in Unmanned Marine Vehicles. The
Institute of Electrical Engineers, 2006.
[2] Fossen, T.I Nonlinear Modelling and Control
of Underwater Vehicles, PhD Thesis.
Norwegian Institute of Technology, 1991.
[3] Fossen, T.I Handbook of Marine Craft
Hydrodynamics and Motion Control. John
Wiley and Sons Inc. 2011.
[4] Fossen, T.I Marine Control Systems –
Guidance, Navigation and Control of Ships,