INTELLIGENT SYSTEM DESIGN FOR MECHATRONIC
APPLICATIONS
“ReSCUe - Me”
I.
PREFACE
Mechanical system has been widely used in many industries, such as steel
manufacturer, automotive industry, electrical power generator industry, cement industry
and paper industry as well as pulp industry. Besides that, it has been improved in
agricultural mechanism industry [1 – 9]. Usually those mechanic systems can stand
alone, but they are controlled by a set of automatic equipment. This equipment is
divided in many different modules that work electrically together. As the result, using a
set of automatic electronic can operate those mechanical systems. Mechanical systems
which are controlled by usual electronic system can be named as mechatronic system.
1.1. BACKGROUND
As mention above, mechatronic system is a system that combines two kinds of
designing field both mechanic and electronic. Mechatronic system has been widely
improved in designing system area, known as mechanical system, such as turbo pump
system and pneumatic valve in hydraulic devices, boiler system in generator, moving
wheel driver system, factories, which all of them are controlled by electronic devices.
Because mechatronic system works by combining physical signal value such as
velocity, acceleration, temperature, stress, load, etc with electrical signal value, the
familiar problem that can be the result is which sensor could be best used to interface
between those two different kinds of signals.
Sensor is component that is easily influenced by many disturbance factors. As the
result, the information from that sensor usually is not the actual information because it
contains many noisily signals. Usually, in mechatronic system uses more than one
sensor so additional noises can be exist and disturb the work of a system. In order that,it
needs an intelligent signal processing system which can interpret each signal of
information that is generated by the sensors to obtain an exactly decision of work.
Intelligent system is a system that can absorb some of the intelligent level of
human. Some of the intelligent level that is generally found in this system are the
ability to be trained, flashback memory, the ability to process data to obtain an exact
working system according to matter which it has trained and the ability to absorb the
intelligence of an expert through written command in a specific language program.
Intelligent system that generally used in application of regulator is decision support
systems basis on knowledge, neural network and fuzzy logic systems.
The effectively of intelligent, which is designed in this “ReSCue-Me”, will be
tested to a small vehicle, which can move automatically and autonomously. With the
hope, if this application works successfully, with a small modification the system will
be able to use in other mechatronic applications such as electrical power generator
station, automotive system, industry of automation system, agricultural mechanism
designed of field and other applications.
1.2. POINTING PROBLEM
Problems that will be met in the effort to design intelligent system for application
in mechatronic field are:
1. The integration between physical mechanism value of signals such as velocity,
both rotational and linier acceleration, temperature, air stress and mechanic, voice
wave, light, etc, with electrical signal needs both calibration and accurateness to
avoid high scale wrong measurement
2. Sensor is component that is easily influenced by many disturbance factors,
especially by high frequency. Generally intelligent system uses a mount of
sensors. So the more sensors are used the higher the disturbances affect to the
system that later will influence the work of the system.
3. Level of intelligent system that will be designed is depending on the complexity
of its algorithm and strategy of regulation. The more intelligent an electronic
system the more spare the algorithm of regulation, besides that, nowadays the
availability of micro controller is quiet sufficient in capacity of algorithm.
According to those problems that must be faced, the conclusions that focusing to them,
as mention below:
1. Sensor or transducer which changes physical value to electrical signal must be
chosen well and accurately as well as it is provided with electronic instrument that
can reduce the outside effects.
2
2. The process of transferring from electric analog signal to digital signal or the
opposite that refers an accurate calibration.
1.3. ADVANTAGE AND BENEFIT OF THE RESEARCH
The research “Rescue-Me” has several purposes and orientations can be described both
in short term purposes and in long term purposes that.
Sort Term Purposes
This research has several orientations in short term purposes as described below:
1. To design an intelligent system that can solve problems that cannot be solved by
other conventional systems and can be widely used in mechatronic fields.
2. To design a system that can access data from different kind of sensors and provided
with electronic instruments that have ability to filter all noises from outside so the
system can work effectively.
3. To design efficiently an intelligent control algorithm with using multiprocessors, so
the program of control strategy which is written in programming language can be
loaded into the memory of micro controllers that works as the main block of the
intelligent system.
Long Term Purposes
Several orientations of the long-term purposes that will be obtained after the research
“RESCUE-ME” success are:
1. If this research is success and can be tested to a small vehicle system which has
intelligence both in avoiding obstacles and in identifying well a specific object, so it
can be hoped that this intelligent system can be applicated in other mechatronic
systems (Look at figure 1).
2. This research,“RESCUE-ME”, uses designing approach and could be hoped that it
can come to an intellectual property result or patent that has benefit in mechatronic
application.
3
IC 2
IC 1
The
circuit of
Smart
Circuit
7805
IN
1
If succeed, modification
of controller for other
useable special
application
7805
GND
OUT
2
3
IN
GND
2
1
OUT
3
10 Kohm/1/4W
100uf/
50V
100uf/
50V
100uf/
50V
10Kohm/1/4W
10Kohm/1/4W
10Kohm/1/4W
10 K ¼ W 10 uF/50 V
Vcc +5V
SW 7
P1.0
1
P1.1
SW 1
P1.2
P1.3
P1.4
P1.5
P1.6
P1.7
RST
P3.0
P3.1
P3.2
P3.3
P3.4
P3.5
P3.6
SW 4
SW 3
SW 2
SW 5
SW 6
SW 8
SW 9
VCC
P0.0
20
19
P0.1
P0.2
P0.3
P0.4
P0.5
34
P0.6
33
P0.7
18
17
16
15
14
13
12
11
40
2
39
3
38
4
37
5
36
6
35
7
8
9
32
10
11
31
IC 3
ALE / PROG
PSEN
P2.7
P2.6
P2.5
26
P2.4
29
28
27
16
P3.7
17
Crystal 11.0592 MHz XTAL1
18
30 pF
XTAL2
19
30 pF
GND
20
1
2
IC 4
74LS573
3
4
5
6
7
8
9
10
30
12
13
14
89C51
15
EA / VPP
25
24
23
22
21
470 ohm
P2.3
P2.2
470 ohm
+ 5V
330 ohm
P2.1
P2.0
Vcc +12 V
1
IN 4002
10 ohm/1/4w
NC
SW 10
2
3
NC
10 ohm/2w
1
8
IC 5
IN A
2
Aki
Kering
+
GND
-
IN B
NC
IN A
Out A
GND
7
TC 4424
VDD
3
6
4
5
IN B
Out B
1
2
8
IC 6
7
TC 4424
3
6
4
5
M1
NC
Out A
VDD
Out B
M2
Tested on small
vehicle
Potential
applications
Electric Power
Generator Field
Mechanizati
on of
agricultural
Field
Automation
of Factory
Field
Small Vehicle
Automotive
Industry
Field
Other
Mechatronic
Fields
Figure.1. Potentials of application of Intelligent System in Mechatronic Fields.
II. LIBRARY VIEWS
2.1. INTELLIGENT SYSTEM
Intelligent system is a system that can adopt a small part of human intelligent
level that uses to interact external condition of the system. The small part of human
intelligentsia such as ability to be trained, ability to remember or flash memory, ability
to process data in order to act exactly according to matters which have been trained and
ability to absorb the intelligence of an expert through written command in a specific
programming language. Sub of chapters which followed will explain briefly three of
intelligent systems that is specifically mentioned
2.1.1. Intelligent System Bases on Knowledge
An intelligent system base on knowledge is a system that is able to absorb the
intelligentsia of an expert. This systems can be drawn as figure 2 which has a main
block as bases of knowledge contains the expertisely information. The intelligent
information can be interpreted in intelligent algorithm and component of precondition
signal. This intelligent algorithm will decide the exact action for each condition or
status of the system.
Sample of the intelligentsia which can be stored by this system are the
intelligentsia of avoiding obstacles, the intelligentsia of injecting fertilizer powder with
correct composition [2], the intelligentsia of separating object according to specific
classification [3] or the intelligentsia of recognizing well the condition of agricultural
4
products object which will be produced [4]. Off course, this intelligentsia must be
provided by sufficient precision of sensor.
INTELLIGENT SYSTEM BASES ON KNOWLEDGE
Bases of
Knowledge
Signal
Precondition
Signal
Precondition
Intelligent
Algorithm
Output signal
Feedback signal
Plant
System/Process
Decision
Status of
Process
Figure.2. Knowledge-based Intelligent System.
2.1.2. Fuzzy Logic System
Fuzzy logic System is a system that adopts control strategy of fuzzy logic
inferention. Fuzzy logic inferention processes external datas by using membership
functions which are obscure.
Fuzzy logic system has been widely used in mechatronic application for
agriculture such as for detecting the weight level of nitrogen of earth production by
using multi-spectral sensor [7]. Besides that, this system is also effectively used to
Output
Defuzification Circuit
Input 1
Fuzificztion
Fuzification
Control
Circuit of
Fuzzy
Inferention
Input 2
Input 2
Measurement
through sensore
Input 1
control the movement of robot [8].
Decision of
control signal
Fuzzy Logic System
Plant
Process
Figure.3. Structure of Fuzzy Logic System.
5
2.1.3. System Bases on Artificial Neural Network
The inspiration of neural network comes from the organization if human brain
which consists of a million varieties of nervous cells. Neuron is a special nervous cell
that delivers electrical signal. About 10 percent among of the whole cells are neuron or
it is about 10 million of neurons in the human brain. Each of neuron interacts to one
another through a contact, called as synapses. Each neuron receives signal from a
million of synapses in average. So the structure of brain is built of a huge number of
neuron networks. Artificial Neural Network in multilayers sctructure can be illuistrated
in Figure 4(a) in which a number of neurons is connected by synapsises. The
enlargement of this network shows two neurons which are connected by a synapsis.
Neuron has two kinds of operation, both for adding process of weight synapsis signals
() and for activating nonlinieristic operations (). Synapsis sends signal from a
neuron to another one next with weight wij which can be adjusted through training
procedure.
Sinaptik
Lapisan Hidden
Sinaptik
Lapisan Output
Input
Jaringan
Output
Jaringan
Unit
Sensor
Neuron
Lapisan
Output
Neuron
Lapisan
Hidden

neuron i
Wij
neuron j

(a)
Neurokontroler
Model Inversi
Isyarat kontrol
Setpoint
Plant
Output
Plant
_
Isyarat latih
Algoritma
Belajar
+
(b)
Figure.4. (a) Structure of Artificial Neural Netywork .
(b) Architecture of system bases on Artificial Neural Network.
6
Figure 4(b) illustrate an architecture of Artificial Neural Network control system
application.
Artificial Neural Network
has been implemented successly in
microprocessor for high presition of cultivating in technology of
agricultural
application [5]. Artificial Neural Network also has been used in simplifying the use of
energy system in hydrolic pump station. [6].
2.2. ELECTRONIC INSTRUMENTATION
Electronic Instrumentation is devices that support the work in processing
electronic signal. Electronic Instrumentation usually provides a configuration of sensors
that change physical values, such as heat, light, voice wave, infrared wave and other
physical signal to become electronic values that are ready to be processed by the main
control circuit (such as micro controller). Nevertheless, Electronic Instrumentation is
also able to change 2 dimensions of visual pictures to be an electronic signal by using
infrared signal [9]. General description about sensors and their electronic
instrumentation that will be used in this research is explained in APPENDIX A.
2.3. MICROCONTROLLER
The main use of micro controller is to control the operation of a machine. Control
strategy for a specific machine is modeled in algorithm program of arrangement. Which
is written in assembly language. Next, The program is translated to a machine code and
then will be saved in a digital memory media, called as ROM (Look at the figure 5).
Design approach of micro controller and microprocessor is the same. So,
microprocessor is classified as part of a micro controller.
Micro controller consists of features that located in a microprocessor, such as
ALU, SP, PC and registers include features of ROM, RAM, parallel input/output and
serial counter. Micro controller that will be used in this research is family of 8051,a
product of ATMEL (AT89C51). A clear and concrete illustration about micro
controller can be figured out in [10] or in APPENDIX B.
7
III. STRUCTURAL AND FUNCTIONAL APPROACHES
3.1. FUNCTIONAL APPROACH
3.1.1. Functional Approach by Artificial Neural Network
According to the explanation in library view, that artificial neural network is
consist of a massive of neuron cells with bigger interconnection of synapses as well.
Figure 5 indicates a model of artificial neural network that is consist of 3 layers, 32
neuron cells that are classified in 9 groups and a number of interconnect synapses
among neurons in different layers.
Neuron and synaptic junction will store important information about the
intellgentsia that is obtained after artificial neural network trained with different pairs
of input-output data that are required. One kind of the algorithm that is known for
training artificial neural network is back propagation algorithm. This algorithm will
change value of the synaptic weights so; the artificial neural network will form a
specific function according to the trained material. So through the neurons and the
weight of synaptic junctions, the artificial neural network uses to store the intelligent
information that is needed in processing signal data according to the matters which are
trained before
2a
3a
4a
5a
1S1
1S2
1S3
1S4
1S5
2S1
2S2
2S3
2S4
2S5
3S1
3S2
3S3
3S4
3S5
4S1
4S2
4S3
4S4
4S5
5S1
5S2
5S3
5S4
5S5
1a
1N1
1N2
1N3
2N1
2N2
2N3
3N1
3N2
3N2
1b
2b
3b
3D1
3D2
3D3
1c
Model
Kendalian
Figure.5. Intelligent system with artificial neural network approach.
8
3.1.2. Functional approach by Fuzzy Logic System
Figure 6 illustrates the architecture of a Fuzzy Logic System (FLS). This FLS
consists of a few components, as examples are Function Membership blocks (NB, NS,
Z, PS, PB), minimum function blocks (Min1, till Min 25), minimum function blocks
(MAX), Multiplier block (Mult), Adder multiply block (ADDER) and divider block
(DIV). Functionally, Those blocks work by executing inferential rules that have been
made until they maintain a decision control output.
C5
C4
C3
C2
IN 2
C1
NB
NS
Z
PS
PB
Mult
Mult
Mult
Mult
ADDER 16 bit
Register for
Fuzzy Singleton
Consequent
Mult
Min1
Min2
Min3
Min4
Min5
NS
Min6
Min7
Min8
Min9
Min10
Z
Min11
Min12
Min13
Min14
Min15
PS
Min16
Min17
Min18
Min19
Min20
PB
Min21
Min22
Min23
Min24
Min25
Z
OUT
Max
Z
Max
Z
Max
Z
Max
Z
-1
-1
-1
ADDER 8 bit
IN 1
NB
Matrix Switches
DIV
-1
Max
-1
CLOCK
Figure.6. Intelligent System by Fuzzy Logic System Approach
3.2. STRUCTURAL APPROACH
The effectively of intelligent system that will be designed, will be tested to a
small vehicle which can move automatically and autonomously. This small vehicle will
be provided with a number of sensors that measure and observe external condition of
the system. Intelligent system which is implemented into three chips of micro controller
(smart circuits) will process intelligently the information from sensor which next will
driver a few of electric motor driver so the vehicle can move by itself.
9
Brain Controller SistemSmart Circuit
MASUKANSINYAL
CLOCK
SENSOR1
SENSOR2
SENSOR3
SENSOR4
+
SENSOR
UN
TU
K
+
SENSOR23
PORTOUTPUTUNTUK
JARINGANjUNCTION
DETEKSISENSOR
BU
S
SE
NS
O
R
SENSOR24
SENSOR25
MIKROKONTROLER
AT89C51
MASTEROFMIKRO-COMPUTER
EEPROM
EXTERNAL
PORTOUTPUTUNTUK
JARINGANDESTINATION
MIKROKONTROLER
AT89C51
PORTOUTPUTUNTUK
SINYALKENDALI
INDIKATOR
LED
PORTOUTPUT
UNTUKINDIKATOR
Sumber
Deteksian
CLOCK
EXTERNAL
11.0592MHZ
MIKROKONTROLER
PORTINPUTUNTUK
AT89C51
SINYALHASIL
INDIKATOR
LED
DRIVER
74LS573
IMPULSKENDALIMOTOR
1
IMPULSKENDALIMOTOR
2
DRIVER
MOTOR1
TC4424
DRIVER
MOTOR2
TC4424
TEGANGANKENDALI
TEGANGANKENDALI
- SEARAH JARUMJAM
- SEARAH JARUMJAM
- BERLAWANANARAH JARUMJAM
MOTORDC
1
RS-380
MOTORDC
2
RS-380
- BERLAWANANARAH JARUMJAM
- DIAM
- DIAM
KENDALIAN
FEEDBACK
SIGNAL
KENDALIAN
Figure.7. Structure of Smart Circuit System.
IV. WORKING INDICATION
Indication, which can be the reference on the succeed of this research “RescueMe”, is the succeed to move a small vehicle that works autonomously by itself which is
provided a set of smart circuit system and implemented in electronic circuit. This
vehicle is provided different kind of sensors and intelligent signal processor that can
process a number of data from sensor to overcome different intelligent decisions
according to the matters of command. So this system can be programmed to execute
specific command both automatically and autonomously.
10
4.1. Detection System
This system must be able to work in parallel with the ability to detect physically
values that will be tested to the system. This value must be able to be converted to
electrical signal according to the level of input that can be caught by the detector
system, in this matter sensor is sure.
4.2 Controller System
This system must be able to interpret control level to be a trained control system
with the ability to think. The plot of control is overcome by learning algorithm with the
complexity of network. The output level of this system already has high precision and
accurateness.
4.3 Controlling System
Control level that can be obtained must be able to control plant (load of control)
according to the purpose of the controller. Work performance of the plant must have
ability to be monitored by the control system, so the plant can work both precisely and
accurately.
With ability of the intelligent system that can be reprogrammed, so the result of
this research “Rescue-Me” also can be applicated in other different kinds of
mechatronic fields such as electric power generator, machines in agricultural
mechanization system, automatic machines in industry, medical mechatronic devices
and other mechatronic area.
V. MANUFACTORY OF PROTOTYPE AND PROCEDURE OF
TESTING
Figure 8 shows the schematic prototype of smart system that is implemented into
three chips of micro controller. Likely, this system is provided with several driver
systems of DC motor and several switches that represent function of the work of sensor.
Algorithm of the arrangement is written in assembly language program that will be
downloaded into the memory of micro controller. Process of writing the prototype of
the program in assembly language will be explained in sub-chapter below.
11
IC 2
IC 1
7805
7805
IN
GND
OUT
1
2
3
100uf/
50V
IN
1
GND
OUT
2
3
100uf/
50V
100uf/
50V
10 Kohm/1/4W
10K ¼W
10K ¼W 10 uF/50V
SW 1
SW 2
10 uF/50V
SW 5
SW 6
P1.4
P1.5
P1.6
P1.7
RST
P3.0
P3.1
P3.2
SW 7
SW 8
SW 9
SW 10
SW 11
P3.3
P3.4
P3.5
P3.6
P3.7
Crystal11.0592MHz XTAL1
30pF
XTAL2
SW 24
SW 25
30pF
IN 4002
2
3
SW 10
P1.5
P1.6
P1.7
RST
P3.0
P3.1
P3.2
P3.3
10 ohm/2w
Aki
Kering
GND
P1.0
P1.1
P1.2
P1.3
P1.4
1
10 ohm/1/4w
10Kohm/1/4W
P1.0
1
P1.1
2
P1.2
3
P1.3
SW 3
SW 4
+
-
Crystal 11.0592MHz
30pF
30pF
P3.4
P3.5
P3.6
P3.7
XTAL1
XTAL2
GND
VCC
P0.0
39
P0.1
38
P0.2
4
P0.3
P0.4
P0.5
34
P0.6
33
P0.7
36
6
35
7
8
9
31
IC3
30
12
29
13
28
14
27
16
25
17
24
18
23
19
22
20
21
89C51 26
15
1
40
2
39
3
38
4
37
5
36
6
35
8
9
IC3
13
28
39
3
38
4
37
5
36
6
35
7
34
8
9
32
27
IC 3
IC 4
74LS573
24
6
7
8
9
10
+ 5V
30
12
29
13
28
27
P2.7
330 ohm
P2.6
89C51 26 P2.5
P2.4
P2.3
24
P2.2
23
P2.1
22
P2.0
330 ohm
25
21
Crystal 11.0592MHz
30pF
NC
INA
GND
INB
1
IC 5
2
8
7
TC4424
3
6
4
5
NC
OutA
VDD
OutB
M2
P2.7
P2.6
P2.4
P2.3
P2.2
23
P2.1
22
P2.0
25
17
1
2
3
4
5
31
11
20
30pF
15
14
13
12
11
P0.5
P0.6
33
P0.7
10
P3.4
14
P3.5
15
P3.6
16
P3.7
17
XTAL1
18
XTAL2
19
GND
VCC
P0.0
P0.1
P0.2
P0.3
20
19
18
17
16
89C51 26 P2.5
16
20
40
2
30
29
19
1
VCC
P0.0
P0.1
P0.2
P0.3
P0.4
31
12
18
P1.6
P1.7
RST
P3.0
P3.1
P3.2
P3.3
P2.0
32
10
15
P2.7
P2.6
P2.5
P2.4
P2.3
P2.2
P2.1
P0.4
P0.5
34
P0.6
33
P0.7
7
14
Vcc +5V
P1.0
P1.1
P1.2
P1.3
P1.4
P1.5
32
10
11
10Kohm/1/4W
10 uF/50V
37
5
11
10K ¼W
40
21
Vcc +12 V
NC
1
INA
2
GND
INB
8
IC6
NC
OutA
7
TC4424
3
6
4
5
VDD
OutB
M2
Figure.8. Electronic Circuit of Smart System.
5.1. PROTOTYPE OF SMART SYSTEM IN MICRO CONTROLLER CHIP
The followed figure 9 shows how to write prototype of the smart rule algorithm
that is written in assembly language into a micro controller. An assembly language
code is converted in binary codes (digital codes) before downloaded into the memory of
micro controller by using special software. This the three chips of micro controller will
perform a complex network. This system plays the main part of Smart Circuit System
that is as the controller of network or can be known as brain control of the system.
12
;**************************************
; Smart System Program „RESCUE-ME”
;**************************************
M1For
equ 0010b
M1Rev
equ 0001b
M1Off
equ 0000b
M2For
equ 1000b
M2Rev
equ 0100b
org 0h
RESET :
Ljmp
M_start
org 23
hint_UART:
reti
orG 30h
M_Start : Mov
P0,#0
MOV
B,#M1FOR
Lcall
SetUpM1
MOV
B,#M2FOR
Lcall
SetUpM2
ORL
A,M1FOR
SETB
P2.7
CLR
P2.6
LJMP
RESTART
…….
……..
…….
SJMP RESTART
AGAIN_P11 :
MOV
R6,#254
again_P1 :
mov
r7,#0ffh
DJNZ
R7,$
DJNZ
R6,lagi_P1
DJNZ
R5,LAGI_P11
LCALL
TOTAL_OFF
RET
R_ONLY : LCALL
TOTAL_OFF
MOV
P0,#0110b
MOV
R5,#20
LCALL
L_ONLY
LCALL
BACKWARD
LCALL
ROTATE
RET
END
Assembler code (assembly language) is
designed by using Compiler of Computer
Software Program Assembler “ASM51”.
010010010101010101
010100110101010101
010101010101000010
101010001011010101
001010110111010101
010101110101010101
010101010010101010
101011111001111110
101010100111010100
000011110101010101
110101010001010101
Compilation of
Assembler code
into Hex/Binary
codes
Downloading proceeds
from Hex/Binary codes
into the memory of micro
controller by using Board
Programmer that
connected to the port of
computer
IC 2
IC 1
7805
7805
IN
GND
OUT
1
2
3
IN
GND
2
1
OUT
3
10 Kohm/1/4W
100uf/
50V
100uf/
50V
100uf/
50V
10Kohm/1/4W
10Kohm/1/4W
10Kohm/1/4W
10 K ¼ W 10 uF/50 V
Vcc +5V
SW 7
P1.0
1
P1.1
SW 1
P1.2
P1.3
P1.4
P1.5
P1.6
P1.7
RST
P3.0
P3.1
P3.2
P3.3
P3.4
P3.5
P3.6
SW 4
SW 3
SW 2
SW 5
SW 6
SW 8
SW 9
VCC
P0.0
20
19
P0.1
P0.2
P0.3
36
P0.4
35
P0.5
34
P0.6
33
P0.7
18
17
16
15
14
13
12
11
40
2
39
3
38
4
37
5
6
7
8
9
32
10
11
EA / VPP
31
IC 3
ALE / PROG
PSEN
P2.7
P2.6
P2.5
26
P2.4
29
28
14
27
16
25
P3.7
17
Crystal 11.0592 MHz XTAL1
18
30 pF
XTAL2
19
30 pF
GND
20
74LS573
3
4
5
6
7
8
9
10
30
12
13
89C51
15
1
2
IC 4
24
23
22
21
470 ohm
P2.3
P2.2
+ 5V
330 ohm
470 ohm
P2.1
P2.0
Vcc +12 V
1
IN 4002
10 ohm/1/4w
NC
SW 10
2
3
NC
10 ohm/2w
1
8
IC 5
IN A
2
Aki
Kering
+
GND
-
IN B
NC
IN A
Out A
GND
7
TC 4424
3
VDD
IN B
6
Out B
4
1
2
8
IC 6
7
TC 4424
3
6
4
5
NC
Out A
VDD
Out B
M2
5
M1
Figure.9. Building process of algorithm rule program into micro controller
2a
3a
4a
5a
1S2
1S1
1S3
1S4
1S5
2S1
2S2
2S3
2S4
2S5
3S1
3S2
3S3
3S4
3S5
4S1
4S2
4S3
4S4
4S5
5S1
5S2
5S3
5S4
5S5
1a
1N1
1N2
1N3
2N1
2N2
2N3
3N1
3N2
3N2
1b
2b
3b
3D1
3D2
3D3
1c
Model
Kendalian
C5
C4
C3
C2
IN 2
NB
NS
Z
PS
PB
NB
Min1
Min2
Min3
Min4
Min5
NS
Min6
Min7
Min8
Min9
Min10
Z
Min11
Min12
Min13
Min14
Min15
PS
Min16
Min17
Min18
Min19
Min20
PB
Min21
Min22
Min23
Min24
Min25
C1
Mult
Mult
Mult
Mult
ADDER 16 bit
Register for
Fuzzy Singleton
Consequent
Artificial
Neural
Network
Model
Mult
Z
Max
Z
Max
Z
Max
Z
Max
Z
-1
OUT
-1
-1
-1
ADDER 8 bit
IN 1
Matrix Switches
DIV
Max
-1
;*********************
*****************
; Smart Cisrcuit Program
„RESCUE-ME”
;*********************
*****************
M1For
equ 0010b
M1Rev
equ 0001b
M1Off
equ 0000b
M2For
equ 1000b
M2Rev
equ 0100b
org 0h
RESET :
Ljmp
M_start
org 23
hint_UART: reti
orG 30h
M_Start :
Mov
P0,#0
MOV
B,#M1FOR
Lcall
………..
…………………………
END
IC 2
IC 1
7805
7805
IN
GND
OUT
1
2
3
IN
GND
2
1
OUT
3
10 Kohm/1/4W
100uf/
50V
100uf/
50V
100uf/
50V
10Kohm/1/4W
10Kohm/1/4W
10Kohm/1/4W
10 K ¼ W 10 uF/50 V
Vcc +5V
SW 7
P1.0
1
P1.1
SW 1
P1.2
P1.3
P1.4
P1.5
P1.6
P1.7
RST
P3.0
P3.1
P3.2
P3.3
P3.4
P3.5
P3.6
SW 4
SW 3
SW 2
SW 5
SW 6
SW 8
SW 9
VCC
P0.0
20
19
P0.1
P0.2
P0.3
P0.4
P0.5
P0.6
33
P0.7
18
17
16
15
14
13
12
11
40
2
39
3
38
4
37
5
36
6
35
7
34
8
9
32
10
11
31
IC 3
12
15
16
20
30
29
13
14
P3.7
17
Crystal 11.0592 MHz XTAL1
18
30 pF
XTAL2
19
30 pF
GND
28
89C51
27
26
25
24
23
22
21
EA / VPP
1
2
IC 4
74LS573
3
4
5
6
7
8
9
10
ALE / PROG
PSEN
P2.7
P2.6
P2.5
P2.4
470 ohm
P2.3
P2.2
470 ohm
+ 5V
330 ohm
P2.1
P2.0
Vcc +12 V
1
IN 4002
10 ohm/1/4w
NC
SW 10
2
3
NC
10 ohm/2w
1
8
IC 5
IN A
2
Aki
Kering
+
GND
-
IN B
NC
IN A
Out A
GND
7
TC 4424
VDD
3
6
4
5
IN B
Out B
1
2
8
IC 6
7
TC 4424
3
6
4
5
NC
Out A
VDD
Out B
M2
M1
Schematic Prototype
smart circuit
CLOCK
Fuzzy Logic Model
Prototype of smart
algorithm in assembly
language
Figure.10. Plot of design of prototype model.
13
5.2. PROCEDURE OF TESTING
According to the explanation visually in figure 1, smart system will be tested its
intelligentsia into a small vehicle. This mini vehicle in hope, can work automatically
and autonomously by the help of smart electronic circuit (smart circuit) which is
attached to the mini vehicle. Figure 11 illustrate the plot of research in which can be
found “algorithm test” procedure and “ reality problem test”. The plot of the research
is initiated by library study until the test of intelligentsia level of the smart circuit
system that have been designed.
Two things would become challenger in testing the algorithm are firstly, how fast
the work of algorithm in responding signals test that represent real condition that will
probably find the control circuit, secondly, How complicated the rule of algorithm that
will decide the size of byte of the program that will be stored in micro controller prior
to the test. One thing must be remembered that micro controller has a limited size of
ROM in storing control program. If the algorithm is too complex to store be stored in
the ROM of micro controller, the design of rule algorithm needs modification in order
to fulfilled the required memory of micro controller
Library Study
Algorithm Design
Algorithm Test
No
Work Performance Test
Complexity Test
Good
Performance?
Too
Complexity?
Yes
Yes
No
Algorithm Implementation
Real Problem Test
Work Sufficiently
and Smartly?
Yes
Conclusion
Figure.11. Plot of research and testing of smart circuit
14
VI. REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
John F. Reid (2001). “Opportunities and Challenges for The Development of
Automated and Autonomous Systems for Agricultural Production”. 2nd IFACCIGR Workshop on Intelligent Control For Agricultural Applications. Bali,
August 22nd-24th, 2001.
Sakae Shibusawa (2001). “Precision Farming Approaches For Small Scale
Farms”.2nd IFAC-CIGR Workshop on Intelligent Control For Agricultural
Applications. Bali, August 22nd-24th, 2001.
K. Hatou, A., Takasuka, and Yasushi Hashimoto(2001). "The Optimalization of
The Fruity Separation Algorithm of Accumulating The Strawberry Automatic
Harvesting Robot”. 2nd IFAC-CIGR Workshop on Intelligent Control For
Agricultural Applications. Bali, August 22nd-24th, 2001.
Noburo Noguchi, John F. Reid, Kazunobu Izhii, and Hideo Terao (2001). “Crop
Status Sensing Based on Machine Vision for Precision Farming”. 2nd IFAC-CIGR
Workshop on Intelligent Control For Agricultural Applications. Bali, August
22nd-24th, 2001.
Haruhiko Murase (2001). “Microprocessor Control For Plant Factories”. 2nd
IFAC-CIGR Workshop on Intelligent Control For Agricultural Applications. Bali,
August 22nd-24th, 2001.
M. Manoliu, R. Toculescu, M. Dragoicea, M. Ralea (2001). „Neural Networks for
Energy Saving in Agricultural Pumping Stations”. 2nd IFAC-CIGR Workshop on
Intelligent Control For Agricultural Applications. Bali, August August 22nd-24th,
2001.
Yunseop Kim, John F. Reid, Qin Zhang (2001). “Fuzzy Control of Multi-Spectral
Imaging Sensor for Detecting Crop Nitrogen Stress”. 2nd IFAC-CIGR Workshop
on Intelligent Control For Agricultural Applications. Bali, August August 22nd24th, 2001.
John F. Reid, Qin Zhang (2001). “A Flexible Fuzzy Controller for Mobile E/H
Systems”. 2nd IFAC-CIGR Workshop on Intelligent Control For Agricultural
Applications. Bali, August 22nd-24th, 2001.
Tony Antonio, Hamdani Zain (2001). “Plant Identification by Near-Infrared to
Visible Spectrum Ratio Techniques”. 2nd IFAC-CIGR Workshop on Intelligent
Control For Agricultural Applications. Bali, August 22nd-24th, 2001.
Mazidi, Muhammad Ali, Mazidi Janice Gillispie (2000). “The 8051
Microcontroller and Embedded Systems”. Penerbit Prentice Hall, Upper Saddle
River, New Jersey, Colombus, Ohio.
N. Noguchi, M. Kise, and John F. Reid (2001). “Autonomous Vehicle Based on
GPS and Inertial Sensors”. 2nd IFAC-CIGR Workshop on Intelligent Control For
Agricultural Applications. Bali, August 22nd-24th, 2001.
Hadary F., dan Ohyama F. (1999). ”Design of Proportional and Derivative Gain
for Feedback Control Technique. Case Study: arm Robot System”. Proceeding
ZOA No. 35, 1 – 7 Juni 2000, Tokyo Institute Japan.
Darwison, Adhi Susanto, F. Soesianto, Hakkun Elmunsyah, Sasongko P. H., and
Abdul Hakim B. (2001). “Optimumm Value in The Design of DC Servomotor
Control to Stepper Motor”. 2nd IFAC-CIGR Workshop on Intelligent Control For
Agricultural Applications. Bali, August 22nd-24th, 2001.
Kuswadi S., Sulistiono I. A., dan Tjahjono A. (2001). “A Computional
Intelligence Approach for Navigation and Control of Autonomous Mobile
Robots”. Dalam jurnal IECI Chapter Japan Series, Vol. 3, No. 1.
15