International Journal
on Marine Navigation
and Safety of Sea Transportation
Volume 2
Number 1
March 2008
Intelligent Ship Control System
A. Lebkowski, R. Smierzchalski, W. Gierusz & K. Dziedzicki
Gdynia Maritime University, Gdynia, Poland
ABSTRACT: The article present intelligent control system of ship motion in situations threatening with
collision. The goal of the presented system is to support the navigator in decision making, with possible full
replacement of his work in the future. In this article, it was introduced system joins work of three computer
techniques, evolutionary algorithms to marking of optimum path of passages, fuzzy logic to control ship after
set path of passage and multivariable robust control to precise movement of the vessel with very small
velocity and any drift angle. Introduced system has to assure safe trip of ship in each navigational conditions
with regard of weather conditions and met navigational objects of static or dynamic nature. For testing of the
operation the system, the marine environment simulator was used to present navigational situations in a 3D
graphical mode at the poor hydro- and meteorological conditions.
Modern marine transport requires preserving dates
of delivery to harbours located all over the world,
irrespective of weather conditions and volumes of
transported cargo. In case of passenger transport, an
additional requirement should be taken into account
which is providing passengers with adequate level of
comfort. On the other hand, there is a tendency to
reduce ship operation costs, and realisation of this
task may unintentionally involve threats to human
life and natural environment. Losing transported
cargo is also possible. That is why securing safety of
sailing is one of more important issues in present-
-time marine navigation.
Among all causes of sea accidents, navigational
errors compose a relatively big group (Soares,
Teixeira 2001). Out of fifteen biggest ships lost in
years 2003-2004, as many as nine cases referred to
collision or stranding (
A method leading to the reduction of sea accident
risk may be introducing solutions that support the
navigator in decision making in the situations
threatening with collisions.
For the voyage of the ship carrying passengers
and cargo to be safe, a passing path meeting safety
conditions should be determined for the ship. A
basic safety condition assumes a minimum distance
Db of safe passing of objects. Besides, the
determined passing path should meet economic
criteria, important for the shipowners, which include
the length of the passing path, time needed to cover
it, changes of ship’s speed along particular path
sections and number of manoeuvres to be performed
by the ship.
Taking into account a defined by the operator
range of observation of a navigational situation (3, 8,
12, 24, 48 sea miles) covered by the ARPA
(Automatic Radar Plotting Aids) system, the time
horizon for solving a navigational problem can range
from several minutes to 1-2 hours. The area of
observation may be obscured by navigational
constraints, of static or dynamic nature, which can
considerably affect the process of the passing path
The task of determining a safe passing path for a
ship at sea is reduced to the selection of an optimum
solution, or a subset of optimum solutions, from a
set of permissible paths. The paths are selected using
an assumed cost criterion, provided safety conditions
are unconditionally met and constraints taken into
account. When determining a safe path for the ship
motion, a compromise solution is searched for. The
compromise is, generally, made between the cost of
trajectory deviation from that assumed, or from the
shortest way leading to the assumed endpoint, and
the safety of passing navigational constraints. In this
situation, steering the ship along the determined path
taking into account parameters of ship dynamics and
meteorological conditions is reduced to determining
a passing trajectory.
In order to determine an optimal passing path for
the ship, a ISCS (Intelligent Ship Control System)
has been developed. The system makes use of united
work of two computer techniques: evolutionary
algorithms (EA) for determining the optimal passing
path, and fuzzy steering for directing the ship along
the assumed path. The trajectory of the ship motion
determined by fuzzy steering along the assumed path
is called a passing trajectory. The information on the
navigational environment is delivered to a moving
ship by a measuring system. The navigational
constraints, both static and dynamic, which ship
meets on its way, compose the navigational
environment and are modelled in the form of
polygons, the shape and dimensions of which
depend on weather conditions, region of navigation,
manoeuvring ability of the ship, its dimensions,
speed, course and bearing line, as well as speeds of
the passed objects. Concluding, the task of the
intelligent ship control system (ISCS) is controlling,
in a fuzzy way, the motion of the ship in the
navigational environment along the passing path
determined in an evolutionary way.
An essential feature of the system is its ability to
control safely and automatically the ship motion in
navigational situations. The use of the system
considerably facilitates operator’s work concerning
calculations performed in order to determine the
passing path for the ship, as well as actions taken to
keep the ship on the already determined passing
path. When performing this function, the system
takes into account all safety related legal regulations.
The proposed solution is expected to contribute in a
considerable way to the reduction in the number of
accidents recorded, and to the increase of the safety
of sea navigation. The article also presents a
simulator used for verifying the operation of
intelligent system controlling ship’s motion at sea.
Analyzed is the operation of the system in the
situation threatening with collision, and in the
presence of unfavorable hydro- and meteorological
conditions. The developed simulator presents
navigational situations using 3D graphics.
The ship, moving in the sea environment, meets
various navigational constraints, of both static and
dynamic nature. The static constraints include lands,
canals, shallows, straits, and/or areas with legal
traffic restrictions (traffic separation areas, water
lanes, etc.). The static navigational constraints are
approximated by polygons in a similar manner to
that used for creating electronic vector maps. The
dynamic constraints include other ships and moving
objects passed by the own ship. These obstacles are
modelled as moving hexahedrons. The area
surrounding the own ship and all approaching
moving objects is called a domain. The dimension of
the domain depends on the navigational situation
and parameters of motion and positions of the own
ship and approaching objects. The positions, speeds
and bearing lines of the approaching objects are
determined by the ARPA system. Part of the
approaching objects create collision threat for the
motion of the own ship. In the evolutionary task of
avoiding collisions it was assumed that the object is
considered dangerous if it has come into the area of
observation and can cross the course of the own ship
at a dangerously close distance, defined by the
operator depending on weather conditions and the
navigation area.
Initial conditions, assumed when determining the
passing path for the ship, include current position of
the own ship and parameters of motion of the strange
objects, determined at the initial instant by ARPA.
The determined trajectory of the ship motion has a
form of a broken line, consisting of line segments,
linking the starting point with the assumed target
determinig optimum
passing path
(wind, sea currents, waves)
navigational environment
(static and dynamic constraints)
Trajectory Controller
Evolutionary Algorithm
Multivariable Robust Controller
very small velocity and drift angle
Correction of the route
of the passage
Evolutionary Algorithm
- bearing line to object j
- course of object j
- speed of object j
- assumed course of own ship
- assumed speed of own ship
- speed control signal
- course control signal
Ψ - ship's course
V - ship's speed
Z - disturbances
Course Controller
compensation of weather conditions
Speed Controller
steering along assumed trajectory
Fig. 1. The structure of ship control in a collision situation with
use of ISCS
Fig. 2. The control subsystems for the ship motion: a) heading
and/or speed stabilisation, b) stabilisation of the turning radius,
c) roll damping, d) trajectory tracking, e) precise steering with
the low speed, f) dynamic ship positioning (DSP), g) automatic
single-buoy mooring, h) turret-mooring for FSO, FPSO or
FPDSO ships
Taking into account the hierarchical manner of
ship steering, shown in Fig. 1, the optimum passing
path (trajectory of motion) is determined by EA
using a kinetic model of ship motion. Ship’s
dynamics is taken into account, when the fuzzy
controller steers the ship along the selected trajectory
of motion.
The adopted structure of steering secures:
At the first stage: determining a safe passing path
for the own ship on the basis of an assumed target
point and current navigational situation in the
area of navigation. The trajectory consists of a
sequence of line segments characterized by
constant course and speed depending on the
navigational situation recorded by the ARPA
At the second stage: steering the own ship along
the selected passing path, time and distance
parameters being preserved and disturbances
acting on the ship taken into account. At the
second stage, steering is determined for a selected
passing path of the ship, taking into account
dynamics of the ship, and hydro- and
meteorological conditions in the navigational
area. This stage consists of three calculation
phases, executed simultaneously: phase one, in
which the deviation from the assumed course is
currently controlled and corrected depending on
the navigational situation and effect of sea
currents, waves and wind (course controller);
phase two, in which the speed of ship motion
along the selected path is controlled (speed
controller); phase three, is realize precise
movement of the vessel with very small velocity
and any drift angle (case in Fig. 2). Such kind of
the motion is used mainly in harbours or other
constrained areas.
At the third stage: adaptive correction of the
passing path in a navigational situation changing
in an unpredicted manner.
The passing path was determined using an
evolutionary algorithm, presented in detail in
(Łebkowski & Śmierzchalski 2003a, Śmierzchalski
1999, Śmierzchalski 2000). On the basis of EA tests
one can conclude that genetic operators are used
with different frequencies during particular phases of
operation of the algorithm (Łebkowski et al., 2005).
In order to increase EA efficiency, its operation was
divided into two phases.
In the first phase, an area of possible solutions is
searched, which contains the location of a global
optimum. EA procedures used in this phase base on
a population consisting of 50 individuals. The task
performed by EA in this phase includes the
examination of the space of permissible solutions.
The individuals composing this population are
characterized by increased probability of the use of
genetic operators, such as: crossing, soft mutation,
and repair of individual, due to the most frequent use
of those operators in the initial phase of operation of
the algorithm.
In the second phase of EA operation, the area of
solutions obtained in the first phase is exploited in
order to obtain an approximation of the global
optimum. EA procedures used in this phase base on
a population consisting of 10 individuals. The
individuals composing this population would be
characterized by increased probability of use of the
genetic operators of mutation, smoothing and gene
The ship is kept on the assumed passing trajectory
using the rules of fuzzy inference, first proposed by
Mamdani (Mamdani 1974). The fuzzy controllers
are characterized by lower sensibility to disturbances
than that revealed by conventional controllers widely
used in naval autopilots. Another quality which
makes fuzzy controllers more effective than their
conventional counterparts is possibility to incorporate
expert’s elements and basis of knowledge into the
controller’s basis of knowledge.
As mentioned above, the fuzzy controller of
ship’s motion is divided into two parts: the course
controller and the speed controller, working
simultaneously. These two parts are structurally
identical. The input signals for the both controllers
are: the deviation of the output value from that
assumed, and speed of the deviation changes in time.
For the course controller the assumed value is the
course, while for the speed controller – speed.
The difference between the fuzzy course
controller and the fuzzy speed controller consists in
the application of different bases of rules in each
controller. For the course controller, 9 linguistic
values have been defined at each input and output.
They are: NH, NM, NL, NVL, Z, PVL, PL, PM, and
PH which, respectively, stand for "Negative High",
"Negative Medium", "Negative Low", "Negative
Very Low", "Zero", "Positive Very Low", "Positive
Low", "Positive Medium", and "Positive High".
Membership functions for rule predecessors and
successors are shown in Fig. 3.
0,00- 0,25- 0,50- 1,00
0 1 2 3- 1- 2- 3 ∆Ψ
4- 4
- 0,75 0,25 0,50 1,000,75
Fig. 3. The shape of the membership function for an 81-rule
fuzzy course controller
For the fuzzy course controller defined in the
above manner 81 possible Mamdani-type rules are
obtained and placed in the controller’s basis of rules.
The output signal from the controller is the signal for
steering the rudder deflection, passed to the steering
engine. A positive/negative value means steering the
ship to the left/right.
For the speed controller 7 linguistic values have
been defined. They are: NH, NM, NL, Z, PL, PM,
PH and stand, respectively, for "Negative High",
"Negative Medium", "Negative Low", "Zero",
"Positive Low", "Positive Medium", and "Positive
High". Membership functions for rule predecessors
and successors are shown in Fig. 4. The fuzzy speed
controller includes 49 Mamdani-type rules placed
in the controller’s basis of rules. The output
signal from the speed controller is the change of
position of the speed adjuster lever on the main
engine speed governor. A positive/negative value
means increase/decrease of rotational speed of the
main engine.
0,00 0,33 0,66 1,00- 0,33- 0,66- 1,00
0 1 2 3- 1- 2- 3 V
Fig. 4. The shape of the membership function for a 49-rule
fuzzy speed controller
The required passing trajectory of the ship,
determined by a two-phase EA and corrected by
the fuzzy controller, should be an optimal trajectory
of the ship motion for given navigational
situation taking into account current hydro- and
meteorological conditions of the own ship
environment. The hydrological conditions, which
include wind, sea currents and waves, considerably
affect the steering generated by the fuzzy controller.
The above weather disturbances are modelled by
equations which defining forces and moments
generated by them (Łebkowski & Śmierzchalski
The main goal of the presented control system is the
precise steering of three ship's velocities: surge,
sway and yaw. The values of velocities during such
manoeuvres are very small (often close to zero) and
therefore standard navigation logs and angular rate
meters are unserviceable due to their poor accuracy.
Kalman filters or observators systems should be used
for estimation. The block diagram of the process is
presented in Fig. 5.
For performing of any manoeuvres the regulator
calculates the two demanded forces τ
and τ
longitudinal and lateral directions and one moment
for turning (in the ship-fixed frame).
Fig. 5. The block diagram of the robust control system
These signals should be distributed in optimal
way between propellers installed on the ship - see
Fig. 2.The system for this purpose is commonly
named 'thrust allocation unit' and should be
created individually for every driving system. One
of the possibilities is the algorithm based on the