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

VCCA-2011

Real-time Control and Hardware-in-the-Loop Simulation of Surface

Vessels for Multitask Missions at Seas

Điều khiển thời gian thực và mô phỏng phần cứng trong vòng lặp các tầu mặt

nước để làm đa nhiệm vụ trên biển

Hung Duc Nguyen

University of Tasmania / Australian Maritime College

e-Mail: nguyenhd@amc.edu.au

Abstract This paper presents an experimental approach to

develop a real-time control system and hardware-in-

the-loop simulation of surface vessels for multitask

missions at seas. A multitask mission control system

has many functions as an autopilot, rudder-roll

damping, speed control, dynamic positioning,

automatic mooring and anchoring, berthing and

unberthing. A model-scaled container vessel is used

for this work. Model-scaled experiments are

conducted using a model test basin in order to verify

feasibility of the automatic multitask mission control

system. The paper first summarises control

algorithms, then describes the experimental facility

and development of real-time control programs.

Tóm tắt: Bài báo này trình bày phương pháp thử

nghiệm phát triển hệ thống điều khiển thời gian thực

và mô phỏng phần cứng trong vòng lặp cho tầu mặt

nước thực hiện đa nhiệm vụ trên biển. Hệ thống điều

khiển thực hiện đa nhiệm vụ có các chức năng như

máy lái tự động, giảm lắc ngang, điều khiển tốc độ,

định vị động, neo buộc tầu tự động và ra vào cầu tự

động. Một tầu mô hình đuợc sử dụng cho công trình

này. Các thí nghiệm mô hình được thực hiện sử dụng

bể thử mô hình nhằm kiểm chứng tính khả thi của hệ

thống điều khiển đa nhiệm vụ. Bài báo trước hết tóm

tắt các thuật toán điều khiển và tiếp theo mô tả thiết bị

thí nghiệm và phát triển chương trình điểu khiển thời

gian thực.

Nomenclature Symbol Unit Meaning

Ud m/s Desired velocity

d rad Desired heading angle

xi, yi m Position coordinates

Abbreviation RRD/S Rudder roll damping/stabilisation

IMO International Maritime Organization

CCP Controllable pitch propeller

LQG Linear quadratic Gaussian

1. Introduction Surface vessels are the main means of marine

transport. New generation surface vessels require

automation at a high level. Design of automatic

control systems for surface vessels involves an

understanding of their manoeuvrability, seakeeping

and seaworthiness. The most important motions for

surface vessels are surge, sway and yaw while

unnecessary motions are heave, pitch and roll.

Small autonomous surface vessels have recently been

applied in various missions in rivers and seas in

remote areas, for example, a river water sample taking

vessel is used to take water samples at certain time

and take measurement of water sample and send data

to the control centre. Another example of autonomous

surface vessel is for littoral surveillance [2].

This article is about the second step to realise an

automatic multitask mission manoeuvring system for

surface vessels. The article focuses on applied aspects

of the system and experimental approach.

The main purpose of this paper is to:

do feasibility study of the automatic multitask

mission manoeuvring systems by computer

simulation;

develop real-time control programs for the

multitask mission manoeuvring system;

describe experimental facilities;

realise multitask mission manoeuvring system;

and

propose applications of autonomous surface

vessels for some missions at remote sea areas

where human being find it difficult to access.

This article is organised as follows: Section 1

Introduction, Section 2 Mathematical background,

Section 3 Brief description of AMC experimental

facilities, Section 4 Software controller diagrams,

Section 4 Development of software controller

programs, Section 5 Design of experiment; Section 6

Possible applications and Section 7 Conclusions.

2. Mathematical Background for

Multitask Mission Manoeuvrves Nguyen [12][14] proposed a multitask mission

manoeuvring system based on the recursive optimal

method in which a recursive estimation algorithm is

combined with an optimal control algorithm. The

main functions of the multitask mission manoeuvring

system are:

autopilot: course keeping and changing;

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rudder-roll stabilisation;

dynamic positioning;

manoeuvre tests and estimation of

manoeuvrability indices;

ship motion information providing and

monitoring;

automatic berthing and unberthing

manoeuvres; and

Automatic mooring and anchoring manoeuvres

The multitask mission manoeuvring system consists

of three subsytems (guidance, navigation and control)

as shown in Fig. 1.

Fig. 1 Three subsystems (guidance, navigation and control)

for a surface vessel

2.1 Guidance System – Waypoint positions/LOS

technique

The guidance system generating a reference trajectory

includes desired courses, speed, way-points and

position is constructed by using the waypoint and

light of sight and exponential decay techniques

[12][14]. The guidance system receives prior

information data, position of waypoints and weather

information. For various missions at seas the guidance

system will generate trajectory for the following

cases:

IMO search and rescue expanding square

pattern and sector pattern;

weather routing navigation trajectory;

trawling trajectory;

dredging trajectory;

subsea pipe and device laying and installation

trajectory; and

seismic survey trajectory.

The outputs of the guidance system often are

Desired way-point positions:

wpt.pos: {(x0,y0), (x1,y1), ..., (xk,yk)} (1)

Desired speeds between way-points:

wpt.speed: Ud = {u0, u1, u2, ..., uk} (2)

Desired heading angles:

wpt.heading: d= { d1

, d2, d3

, ..., dk} (3)

The guidance system also receives navigation signals

from the navigation system and computes errors

including position errors (path tangential tracking and

cross-track errors), heading error and speed error.

2.2 Navigation System

The navigation system has the main function of

providing accurate measurements of position and ship

motion. The navigation is equipped with D-GNSS or

RTK-GNSS and GNSS receivers when the surface

vessel is running along a coast where D-GNSS and

correction signals are available. A gyro- or satellite-

compass is used to measure the ship’s heading.

For autonomous surface vessels running in a lake or

model test basin a 6-DOF IMU device is used. In the

in-door model test basin where there is no GNSS

signal, an indoor navigation device is applied to get

the vessel’s measurement.

The GNSS/IMU signals are often including noisy. A

Kalman filter and/or low-pass filter may be used to

estimate state variables that are not measured and to

remove noisy, respectively. An adaptive observer is

also applied for enhancement of accuracy and

reliability of the obtained signals.

2.3 Control System

As shown in Fig. 1 the control system consists of two

blocks: motion control and controller allocation. The

control system synthesises an appropriate control

algorithm to compute control signals and allocate

control actions by actuators. The control algorithms

can be one of the following:

conventional PID control;

self-tuning PID control algorithm;

recursive optimal control algorithm [11];

optimal control algorithm;

model reference adaptive control;

robust (H-infinity) control;

fuzzy logic PID control;

neural networks-based control; or

genetic algorithm-based control.

The control algorithm adopted in the control system is

often complicated because of MIMO control system

which controls many output variables.

For an automatic multi-task control systems used in

marine vessels equipped with a propeller and rudder

the control program should include the following

control modes:

one control (autopilot) without RRD: course

control by rudder;

one control (autopilot) with RRD: course

control and roll damping by rudder;

two controls (course and speed) by rudder and

engine shaft rpm or CCP pitch angle without

and/or with RRD; and

three controls (course, speed and positions)

without and/or with RRD.

The recursive optimal control algorithm is a

combination of an optimal LQG control law and

recursive identification algorithm. The recursive

identification algorithm is either the recursive least

squares algorithm or the recursive prediction error

algorithm. Interested readers can find more

information on this control algorithm in Appendix 1

and in [12][14].

3. Brief Description of Model-scaled

Vessel and Electronics

Estimated

position and

velocities

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2.1 Model-scaled Container Vessel

In order to develop software control programs and

verify the control algorithms for multitask mission

control systems model-scaled surface vessels

equipped with propulsion system and steering

mechanisms and instrumentation electronics are

needed. It is very ideal if a full-scaled vessel for

experiments is available. However operating a full-

scaled vessel costs a lot of money. A model-scaled

container vessel named “P and O Nedlloyd” is shown

in Fig. 2. The main particulars of the full-scaled

vessel and the model are given in Table 1.

Fig. 2 Model-scaled vessel for experiments

Table 1 Vessel and model main particulars

Full Scale

Vessel

Model (Scale

1:100)

LBP 247 m 2470 mm

B 32 m 320 mm

Draught 12 m 120 mm

Δ 64000 tonnes 62.4 kg

L/B 7.72 7.72

B/T 2.67 2.67

Fig 3 shows onboard electronic devices. The model is

equipped with a twin propeller operated by a dc

motor, rudder controlled by a servo motor and

controller, mass carriage mechanism operated by a dc

motor, a mobile (target) computer (PC\104) with

wireless/Ethernet and DAQ cards, a 6-DOF IMU and

GPS device (Crossbow NAV420CA) and batteries.

The mass carriage mechanism is used to investigate

parametric roll motion and rudder-roll damping

system.

Fig. 3 Onboard electronic devices

As shown in Fig. 3 the target computer communicates

with a host computer via an Ethernet cable or wireless

communication device. In the host computer there is

an integrated environment of software that allows the

user to develop control programs. Software includes

MATLAB/Simulink, Real-time Workshop, RT-LAB

(product of Opal-RT), MS Visual Studio, LabVIEW

and Control Design and Simulation Module and

Python.

2.2 Prototype “GreenLiner” with Electrically-

operated Waterjet

At the AMC propulsion lab there is a prototype of 11-

metre boat equipped with an electrically-operated

waterjet as shown in Fig. 4. The heading is control by

a waterjet nozzle. This prototype allows one man to

ride. GreenLiner’s principal particulars are given in

Table 2.

Fig. 4 GreenLiner, a prototype boat equipped with

electrically-operated waterjet

Table 2 Principal particulars of GreenLiner) Item Original

Spec

48V Electric

Spec

96V HiPo

Config

Built: 1999

Greg Cox,

L.O.A: 7.75 m

L.W.L: 6.15 m TBA TBA

B.O.A: 1.06 m

Draught: 164 mm 244 mm. 200 mm.

Displacement: 348 kg 648 kg. 540 kg.

Powering

Fuel Petrol (a

cup full)

4off 210A-

hr LA

Batteries

8off HiPo

80 A-hr.

LA

Batteries

Engine/

motor

B&S 18 hp

Vanguard

engine

2 cylinder,

4-stroke,

air-cooled.

4hp

HiTorque

Industrial

Technik DC

electric

Motor.

Parallel

fields

10 hp

HiTorque

Industrial

Technik

DC

electric

motor

Series

fields

Construction Ply

(Australian

Plantation

Hoop Pine)

Cruise Speed: 17 knots 7 knots. 11 knots.

Propulsor:

Doen DJ60

water-jet

16B5. 12A4. 16B5.

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In order to develop automatic control systems for

GreenLiner, the current steering system must be

upgraded with an electro-hydraulic steering machine

and data acquisition card.

2.3 Full-scaled and Model-scaled Bluefin

AMC has a training fishing vessel that can be used for

full-scaled experiments as shown in Fig. 5. Main

particulars of full-scaled Bluefin are given in Table 3.

Fig. 5 Full scaled Bluefin

Table 3 Main particulars of Bluefin

Length OA 34.50 m

Length BP 32.00 m

Breadth 10.00 m

Maximum draft 4.40 m

Deadweight 53.60 t

AMC also has a model scaled Bluefin as shown in

Fig. 6.

Fig. 6 Model-scaled Bluefin (scale 1:20)

4. Development of Software Controller

Programs Computer simulation study done with non-linear

mathematical models of two vessels in [11] has

shown the feasibility of the automatic multitask

manoeuvring system using recursive optional control

algorithm. As the second step to realise the multitask

manoeuvring system, model-scaled experiments need

to be conducted to verify the methods.

Real-time measurement and control of a surface

vessel is done by MATLAB/Simulink and RT-LAB

software. The equipment for real-time measurement

and control is shown in Fig. 6. The model-scaled boat

with target computer and electronics is shown in Fig.

9. The target computer is installed with real-time

operating system QNX 6.3 and RT-LAB software,

and the host PC (with Windows) is installed with

MATLAB/Simulink and RT-LAB software.

Controller programs are developed with Simulink. A

sample real-time control program is given in Fig. 8.

Fig. 7 Arrangement of target and host PCs with

sensors and actuators

Fig. 8 Real-time control program developed with Simulink

Target

computer

& DAQ

Host

computer

Ethernet or

wireless

Actuators (propeller motor drive, mass

carriage motor drive, rudder servo

motor controller

Sensors: GPS/6-DOF

IMU, encoders etc.

Sen

sors

: Required software:

MATLAB/Simulink, Real-

time Workshop, RT-LAB

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A real-time control program was made with Simulink

in the RT-LAB environment. An RT-LAB program of

Oral-RT (www.opal-rt.com), fully integrated with

MATLAB/Simulink®, is a real-time simulation

software environment that provides with a

revolutionised way in which model-based design is

performed. Fig. 9 shows the RT-LAB window. The

software required consists of RT-LAB software,

MATLAB/Simulink, Real-time Workshop and a

C/C++ Compiler. Using the RT-LAB software the

real-time control program is made and run in the

following procedure:

create and edit Simulink model

compile the Simulink model to C code;

assign nodes (target) for the Simulink

program;

load the Simulink program to the target

computer, then the user-interface console

window (as shown in Fig. 11) that allows user

to run the control program appears; and

execute the Simulink program.

Fig. 9 RT-LAB Window

Fig. 10 P and O Netlloyd with electronics

Fig. 11 Real-time control program (SC-Console window) developed with Simulink

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A series of real-time control programs have been

developed with Simulink and RT-LAB as follows:

Program 1: Simulink model to control

propeller;

Program 2: Simulink model to control servo

motor (rudder angle);

Program 3: Simulink model to control both

propeller and servo motor

Program 4: Simulink model to receive data

from Crossbow NAV420CA (GPS/IMU)

Program 5: Simulink model to control load

carriage mass to investigate effect of changing

load.

Program 6: Combined program for tasks in 1,

2, 3, 4, and 5 to test functionality of the open-

loop system;

Program 7: Simulink model for autopilot (e.g.

PID control, recursive optimal control);

Program 8: Simulink model for autopilot and

rudder-roll damping, to investigate of mass

carriage mechanism on roll motion;

Program 9: Simulink model for autopilot,

rudder-roll damping and speed control, to

investigate effect of mass carriage mechanism,

speed and course on roll motion (parametric

roll);

Program 10: Simulink model for trajectory

tracking manoeuvres (search and rescue

mission);

Program 11: Simulink model for trajectory

tracking (trawling);

Program 12: Simulink model for automatic

berthing and unberthing manoeuvres;

Program 13: Simulink model for automatic

mooring and anchoring;

Program 14: Simulink model for an integrated

bridge with all above functions;

5. Design of Experiments

Experiments can be conducted using a free-running

model in the AMC model test basin (MTB) (Fig. 11).

Fig. 11 Model test basin and free-running model

Table 4 General specifications of MTB

Length 35 metres

Width 12 metres

Water depth 0 to 1.0 metres

Model towing carriage speed 0 to 3.8 metres/second

Typical model lengths 2 to 6 metres

The MTB has been equipped with the following

ancillary equipment and instrumentation devices:

multi-element wave generator;

non-contact digital video motion capture

system;

variable speed model towing mechanism;

variable speed wind generator;

votating arm mechanism;

multiple wave damping devices;

wide array of single and multi-axis force

transducers;

wide array of wave measurement devices

wide array of video cameras (including

underwater);

acoustic Doppler Velocimeter (measurement

of currents);

pressure transducers;

displacement transducers;

accelerometers; and

multi-channel digital data acquisition systems.

The following experiments will be conducted:

Experiment 1: zigzag test (open-loop system);

Experiment 2: turning circle test (open-loop

system);

Experiment 3: Course keeping and changing

(autopilot);

Experiment 4: Autopilot and rudder-roll

damping

Experiment 5: Autopilot, rudder-roll damping

and speed control;

Experiment 6: Trajectory tracking control for

search and rescue mission;

Experiment 7: Trajectory tracking control for

trawling;

Experiment 8: Automatic berthing and

unberthing manoeuvres; and

Experiment 9: Automatic mooring and

anchoring manoeuvres.

Some proposed experiments are shown in Fig. 12

through 15.

Fig. 12 IMO expanding square pattern with 1 and 2

controls

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Fig. 13 IMO sector search pattern with 1 and 2 controls

Fig. 14 Rudder roll stabilisation to reduce parametric roll

in head seas

Fig. 15 Berthing and unberthing

6. Possible Applications The automatic multitask manoeuvring system is

suggested to be working in some modes as follows

autopilot and RRD at high seas;

the function of manoeuvres for maritime

search and rescue mission should be

compulsory for all merchant vessels in order

to enhance safety at seas;

manoeuvring information and monitoring

system for the captain (deck officers) and

pilot; and

manoeuvrability test system.

Fig. 17 illustrates a proposed multi-task automatic

manoeuvring system with an LCD and Control Panel

in which there are different working mode buttons

and keyboard. The multitask manoeuvring system can

be developed with a microcontroller and/or embedded

computer.

Fig 17 A proposed application for various modes (AUTO =

autopilot, RRS = rudder-roll stabiliser, MT =

manoeuvrability tests, SAR = maritime search and rescue

mission, INFO = information when manoeuvring, GNSS =

global navigation satellite system receiver

In addition to the above proposed system the

multitask manoeuvring system can be developed

further to the following for educational and research

purposes:

automatic berthing/unberthing system;

automatic mooring and anchoring system;

dynamic positioning system;

water sample taker;

spilling area measuring autonomous vessel;

power control and management system.

In comparison with ROVs/AUVs, advantages of using

autonomous surface vessels for various missions at

seas are:

solution to energy issue;

solar energy; and

difficulty in communication between the target

computer and the host computer.

7. Conclusions In conclusion the paper has discussed the following

points:

background of the multitask manoeuvring

system;

description of experimental facilities;

development of software controller programs;

proposed experiments using model-scaled

vessel and model test basin; and

proposal of possible applications.

Recommendations for future work are:

continue to develop real-time control programs

with Simulink and LabVIEW;

conduct model-scaled experiments and collect

data for analysis;

analyse experimental data and develop

nonlinear mathematical models for vessels;

and

develop hardware and software for a multitask

mission manoeuvring system and test its

functionalities under lab conditions.

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Acknowledgements This paper is a continuity of the AMC IGS granted

research project financially supported by the AMC

Research and Higher Degrees by Research Committee

during 2005-2007. The author would like to thank the

Research Office for financial support.

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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,

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

of marine vehicles and dynamics of marine vehicles.

Appendix 1 Summary of Control

Algorithms The desired trajectory is one of the following

manoeuvres:

IMO expanding square pattern for search and

rescue mission (Fig. A1);

IMO sector pattern for search and rescue

mission (Fig. A2)

Williams’ turning circle manoeuvre;

Any trawling trajectory; and

Any planned manoeuvres;

The reference trajectory generator in the guidance

system is a vessel simulator using the Nomoto’s first-

order manoeuvring model. Details can be found in

[12][14].

The desired heading angle d is calculated by the

LOS technique as follows:

k+1

dk

k 1

y yatan2

x x

(A1)

When the ship is moving along the desired trajectory,

a switching mechanism for selecting the next way-

point is necessary. The next way-point (xk+1,yk+1) is

selected when the ship lies within a circle of

acceptance with a radius R0 around the current

waypoint (xk,yk) satisfying:

2 2 2

k k 0x x y y R (A2)

Fig. A1 IMO expanding square pattern

The value of R0 is often chosen as two ship lengths,

i.e. R0 = 2Lpp in [8][12][14].

A reference trajectory generator using a vessel

simulator is constructed. The vessel model used in

this paper is of Nomoto’s first-order model with

forward speed dynamics and described as follows:

d d dx U cos (A3)

d d dy U sin (A4)

where (xd,yd) is the desired position, Ud > 0 is the

desired speed and ψd is the desired heading. The

forward speed dynamics is

2

x d w d d xm m U 0.5 C AU (A5)

Fig. A2 IMO sector pattern

Fig. A3 LOS technique

where ρw is the density of sea water, Cd is the drag

coefficient, A is the projected cross-sectional area of

the submerged hull of ship in the x-direction, and (m

– mx) is the mass included hydrodynamic added

mass. The course dynamics is chosen as

d dr (A6)

d d rTr r K (A7)

where T and K are ship manoeuvrability indices, rd is

the desired yaw rate and δr is rudder angle. The

guidance system has two inputs, thrust τx and rudder

angle δr. The guidance controllers can be chosen as PI

and/or PID types.

When the ship goes along the desired trajectory, the

reference heading angle can be adjusted by the

exponential decay technique as shown in Fig. A4.

Heading and position errors when the ship is moving

along the desired trajectory are calculated as follows

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

VCCA-2011

1 d d

2 d d

3 d

e (x x)cos (y y)sin

e (x x)sin (y y)cos

e

e (A8)

where e1 = path tangential tracking error

e2 = cross-track error (normal to path)

e3 = heading error

Fig. A4. Exponential decay technique

If the rudder-roll damping controller is switched on

the vector of errors including roll error (e4) becomes

1 d d

2 d d

3 d

4

e (x x) cos (y y)sin

e (x x)sin (y y) cos

e

e 0

e

(A9)

If the speed controller is on the speed error will be

calculated

5 de U U (A10)

Recursive Optimal Control Algorithm

In order to design control systems with multitask

missions, mathematical models for the steering and

manoeuvring dynamics are applied. For example, the

ship steering dynamics for the automatic manoeuvring

system is represented by an MAXR as follows

(t 1) ( ) (t) ( ) (t) x F θ x G θ u (A11)

(t) ( ) (t)y C θ x (A12)

where x(t) is the state vector, u(t) is the input vector,

y(t) the output vector and F(θ), G(θ) and C(θ) are

system matrices dependent on parameter vector θ.

The unknown system parameters are estimated by one

of appropriate recursive estimation methods. An

optimal control law is applied. The optimal recursive

control algorithm is illustrated by the flowchart as

shown Fig. A5.

Summary of RPE Algorithm: The RPE algorithm is to

minimize the following criterion function:

T 11V t t t

2

θ ε Λ ε (A13)

where Λ is a positive definite matrix, and Gauss-

Newton search direction is chosen as:

1 1f t t t, t, H ψ θ Λ ε θ (A14)

where H(t) is the Hessian, the second derivative of the

criterion function with respect to θ and ψ(t,θ) is the

gradient of the predicted output with respect to θ and

ε(t,θ) is the vector of the predicted errors. The RPE

algorithm consists of the following steps:

Fig. A5 Flowchart for the optimal recursive control

algorithm [12]

Step 1: Calculate the predicted error vector using

ˆt t t ε y y (A15)

Step 2: Update the weighting matrix by

Tt t 1 t t t t 1 Λ Λ ε ε Λ (A16)

Step 3: Update the Hessian:

1 Tt t 1 t t t t t 1 H H ψ Λ ψ H (A17)

Step 4: Update the estimated parameters:

1 1t t 1 t t t t t θ θ H ψ Λ ε (A18)

Step 5: Update the predicted output:

Tˆ t t ty (A19)

Step 6: Calculate the gradient of the predicted output

by

d

ˆt t,d

y

(A20)

Step 7: Update data and loop back to Step 1.

Note that the step size factor α(t) is calculated as

1

t1 t

(A21)

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