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TABLE OF CONTENTS
Cover
Greetings and Thanks from the General Chair
Foreword from Head of Department of Electrical Engineering,
Foreword from Dean, Faculty of Engineering
Organizing Committee
Steering Committee
Technical Program Committee
Keynote Speaker’s Biography
Conference Program
Keynote’s Papers
Author Index
KEYNOTE SPEAKERS
I-1 Multi-User MIMO Wireless System -From Theory to Chip Design Prof. Hiroshi Ochi
1
I-2 Challenges and Opportunities in Designing Internet of Things Prof. Dr. Trio Adiono
11
I-3 Role of Telecommunication Satellite in Indonesia Adi Rahman Adiwoso
13
CIRCUITS AND SYSTEMS
CC1 Enhancement of DRAMs Performance using Resonant Tunneling Diode Buffer Ahmed LutfiElgreatly, Ahmed AhmedShaaban, El-Sayed M. El-Rabaie
14
CC2 Real-time SoC Architecture and Implementation of Variable Speech PDF based Noise Cancellation System Aditya Ferry Ardyanto, Idham Hafizh, Septian Gilang Permana Putra, Trio Adiono
19
CC3 Application of Supervised Learning in Grain Dryer Technology Recirculation Type Cooperated with Wireless Sensor Network Sidiq Syamsul Hidayat, TotokPrasetyo, Amin Suharjono, Kurnianingsih,
Muhammad Anif
24
CC4 Design of Real-Time Gas Monitoring System Based-on Wireless Sensor Networks for Merapi Volcano B. Supriyo, S.S.Hidayat, A. Suharjono, M.Anif, Sorja Koesuma
28
CC5 ANFIS Application for Calculating Inverse Kinematics of Programmable Universal Machine for Assembly (PUMA) Robot Hugo Adeodatus Hendarto, Munadi, Joga Dharma Setiawan
33
CC6 MRC NN Controller for Arm Robot Manipulator M. Khairudin, Nur Kholis
39
CC7 Development of Microcontroller-based Stereoscopic Camera Rig Positioning System Julian Ilham, Wan-Young Chung
44
CC8 Design of A Digital PI Controller for Room Temperature on Wireless Sensor and Actuator Network (WSAN) System Bambang Sugiarto, ElanDjaelani
50
CC9 Display and Interface of wireless EMG measurements Kevin Eka Pramudita, F. Budi Setiawan, Siswanto
56
CC10 Accuracy Enhancement of Pickett Tunnelling Barrier Memristor Model Ahmad A. Daoud, Ahmed A. Shaaban, Sherif M. Abuelenin
61
CC11 Data Fusion and Switching Function For UAV Quadrotor Navigation System Muhammad Faris, Adha Imam Cahyadi, Hanung Adi Nugroho
66
CC12 Data logger Management Software Design for Maintenance and Utility in Remote Devi Munandar, Djohar Syamsi
72
CC13 Investigation of Electrical Properties of NanofibrePolyaniline Synthesize as Material for Sensor Ngurah Ayu Ketut Umiati, Siti Nurrahmi, Kuwat Triyana, Kamsul Abraha
77
CC14 Reconfigurable Floating Point Adder Vipin Gemini
81
CC15 HOVER POSITION CONTROL WITH FUZZY LOGIC Nia Maharani Raharja ,Iswanto, Muhammad Faris, Adha Imam Cahyadi.
87
CC16 METHODOLOGY OF FUZZY LOGIC WITH MAMDANI FUZZI MODELS APPLIED TO THE MICROCONTROLLER Indra Sakti
91
CC17 Fall Detection System Using Accelerometer and Gyroscope Based on Smartphone Arkham Zahri Rakhman, Lukito Edi Nugroho, Widyawan, Kurnianingsih
97
CC18 Design and Implementation of Sensor Fusion for Inertia Measurement on Flying Robot Case Study: Hexacopter Huda Ubaya, Afdhal Akrom
103
CC19 Triple Band Bandpass Filter With Cascade Tri Section Stepped Impedance Resonator Gunawan Wibisono, Tierta Syafraditya
109
CC20 Temperature Response Analysis Based on Pulse Width Irradiation of 2.45 GHz Microwave Hyperthermia Imam Santoso, Thomas Sri Widodo, Adhi Susanto, Maesadjie Tjokronagoro
113
IMAGE PROCESSING AND MULTIMEDIA
IP1 Visual Object Tracking using Particle Clustering Harindra Wisnu Pradhana
117
IP2 Selective Encryption of video MPEG use RSA Algorithm Prati Hutari Gani, Maman Abdurohman
122
IP3 Analytical Hierarchy Process for Land Suitability Analysis Rahmat Sholeh, Fahrul Agus, and Heliza Rahmania Hatta
127
IP4 Training Support for Pouring Task in Casting Process using Stereoscopic Video See-through Display - Presentation of Molten Metal Flow Simulation Based on Captured Task Motion Kazuyo IWAMOTO, Hitoshi TOKUNAGA, Toshimitsu OKANE
131
IP5 Feature Extraction and Classification of Heart Sound based on Autoregressive Power Spectral Density Laurentius Kuncoro Probo Saputra, Hanung Adi Nugroho, Meirista Wulandari
137
IP6 Smart-Meter based on current transient signal signature and constructive backpropagation method Mat Syai’in, M.F. Adiatmoko, Isa Rachman, L. Subiyanto, Koko Hutoro, Ontoseno Penangsang, Adi Soeprijanto
142
IP7 AUTOMATIC DOORSTOP SAFETY SYSTEM BASED ON IMAGE PROCESSING WITH WEBCAM AND SCANNER Stanley Suryono Wibisono, Florentinus Budi Setiawan
148
IP8 Palmprint Identification for User Verification based on Line Detection and Local Standard Deviation Bagas Sakamulia Prakoso, Ivanna K. Timotius, Iwan Setyawan
153
IP9 Cerebellar Model Articulation Controller (CMAC) for Sequential Images Coding Muhamad Iradat Achmad, Hanung Adinugroho, Adhi Susanto
158
IP10 A Comparative Study on Signature Recognition Ignatia Dhian Estu Karisma Ratri, Hanung Adi Nugroho, Teguh Bharata Adji
165
IP11 Study of Environmental Condition Using Wavelet Decomposition Based on Infrared Image S. R. Sulistiyanti, M. Komarudin, L. Hakim, A. Yudamson
170
IP12 Very High Throughput WLAN System for Ultra HD 4K Video Streaming Wahyul Amien Syafei, Masayuki Kurosaki, and Hiroshi Ochi
175
IP13 Iris Recognition Analysis Using Biorthogonal Wavelets Tranform for Feature Extraction R. Rizal Isnanto
181
INFORMATION AND COMPUTER TECHNOLOGIES
ICT1 The Development of 3D Educational Game to Maximize Children’s Memory Dania Eridani, Paulus Insap Santosa
187
ICT2 The Influence of Knowledge Management to Succesful Collaborative Design Yani Rahmawati, Christiono Utomo
192
ICT3 Knowledge and Protocol on Collaborative Design Selection Christiono Utomo, Yani Rahmawati
198
ICT4 Mobile-Based Learning Design with Android Development Tools Oky Dwi Nurhayati, Kurniawan Teguh M
202
ICT5 A mobile diabetes educational system for Fasting Type 2 Diabetes in Saudi Arabia Mohammed Alotaibi
207
ICT6 Aggressive Web Application Honeypot for Exposing Attackerâ€ںs Identity Supeno Djanali, FX Arunanto, Baskoro Adi Pratomo, Abdurrazak Baihaqi,
Hudan Studiawan, Ary Mazharuddin Shiddiqi
211
ICT7 Adjustment Levels for Intelligent Tutoring System using Modified Items Response Theory Ika Widiastuti, Nurul Zainal Fanani
216
ICT8 Smile Recognition System based on Lip Corners Identification Eduard Royce, Iwan Setyawan, Ivanna K. Timotius
221
ICT9 An Integrated Framework for Measuring Information System Success Considering the Impact of Culture in Indonesia Siti Mardiana
225
ICT10 Pre-Processing Optimization on Sound Detector Application AudiTion (Android Based Supporting Media for the Deaf) Gian Gautama, Imanuel Widjaja, Michael Aditya Sutiono, Jovan Anggara, Hugeng
232
ICT11 EVALUATION OF DISTRIBUTION NETWORK RELIABILITY INDEX USING LOOP RESTORATION SCHEME Daniar Fahmi, Abdillah F. I., IGN Satriyadi Hernanda, Dimas Anton
Asfani
238
ICT12 Efficient Message Security Based Hyper Elliptic Curve Cryptosystem (HECC) for Mobile Instant Messenger Putra Wanda, Selo, Bimo Sunafri Hantono
244
ICT13 Application of Web-Based Information System in Production Process of Batik Industry Design Division Indah Soesanti
249
ICT14 Managing and Retrieval of Cultural Heritage Multimedia Collection Using Ontology Albaar Rubhasy, A.A.G. Yudhi Paramartha, Indra Budi, Zainal A. Hasibuan
254
ICT15 Individual Decision Model for Urban Regional Land Planning Agus Fahrul, Sumaryono, Subagyo Lambang, Ruchaemi Afif
259
ICT16 Enhancing Online Expert System Consultation Service with Short Message Service Interface Istiadi, Emma Budi Sulistiarini ,Guntur Dharma Putra
265
ICT17 Mobile Nutrition Recommendation System For 0-2 Year Infant Ratih Nur Esti Anggraini, Siti Rochimah, Kessya Din Dalmi
271
ICT18 Comparison of Distance and Dissimilarity Measures for Clustering Data with Mix Attribute Types Hermawan Prasetyo, Ayu Purwarianti
275
ICT19 Determining E-commerce Adoption Level by SMEs in Indonesia Based on Customer-Oriented Benefits Evi Triandini, Daniel Siahaan, Arif Djunaidy
280
ICT20 Providing Information Sources Domain for Information Seeking Agent From Organizing Knowledge Istiadi, Lukito Edi Nugroho, Paulus Insap Santosa
285
ICT21 Decision Support System For Stock Trading Using Decision Tree Technical Analysis Indicators and Its Sensitivity Profitability Analysis F.X. Satriyo D. Nugroho, Teguh Bharata Adji, Silmi Fauziati
290
ICT22 Design Web Service Academic Information System Based Multiplatform Meta Lara Pandini, Zainal Arifin and Dyna Marisa Khairina
296
ICT23 Effects of VANET's Attributes on Network Performance Agung B. Prasetijo, Sami S. Alwakeel and Hesham A. Altwaijry
302
ICT24 Visualization of Condition Irrigation Building and Canal Using Web GIS Application Falahah, Defrin Karisia Ayuningtias
308
ICT25 Comparison of three back-propagation architectures for interactive animal names utterance learning Ajub Ajulian Zahra Macrina and Achmad Hidayatno
314
ICT26 WORK IN PROGRESS – OPEN EDUCATION METRIC (OEM) : DEVELOPING WEB-BASED METRIC TO MEASURE OPEN EDUCATION SERVICES QUALITY Priyogi B., Nan Cenka B. A., Paramartha A.A.G.Y. &Rubhasy A.
318
POWER SYSTEMS
PS1 Design and Implementation of Solar Power as Battery Charger Using Incremental Conductance Current Control Method based on dsPIC30F4012 Ahmad Musa, Leonardus H. Pratomo, Felix Y. Setiono
323
PS2 An Adaptive Neuro Fuzzy Inference System for Fault Detection in Transformers by Analyzing Dissolved Gases Ms. Alamuru Vani, Dr. Pessapaty Sree Rama Chandra Murthy
327
PS3 Optimal Power Flow based upon Genetic Algorithm deploying Optimum Mutation and Elitism M. Usman Aslam, Muhammad Usman Cheema, Muhammad Samran, Muhammad Bilal Cheema
333
PS4 Design Analysis and Optimization of Ground Grid Mesh of Extra High Voltage Substation Using an Intelligent Software M. Usman Aslam, Muhammad Usman Cheema, Muhammad Samran,
Muhammad Bilal Cheema
338
PS5 Design and Simulation of Neural Network Predictive Controller Pitch-Angle Permanent Magnetic Synchrounous Generator Wind Turbine Variable Pitch System Suyanto, Soedibyo, Aji Akbar Firdaus
345
PS6 Inverse Clarke Transformation based Control Method of a Three-Phase Inverter for PV-Grid Systems Slamet Riyadi
350
PS7 Control of a Single Phase Boost Inverter with the Combination of Proportional Integrator and Hysteresis Controller Felix Yustian Setiono
355
PS8 A Simple Three-phase Three-wire Voltage Disturbance Compensator Hanny H. Tumbelaka
360
PS9 Analysis of Protection Failure Effect and Relay Coordination on Reliability Index I.G.N Satriyadi Hernanda, Evril N. Kartinisari, Dimas Anton Asfani, Daniar Fahmi
365
PS10 Extreme Learning Machine Approach to Estimate Hourly Solar Radiation On Horizontal Surface (PV) in Surabaya –East Java Imam Abadi, Adi Soeprijanto, Ali Musyafa’
370
PS11 Maximum Power Point Tracking Control for Stand-Alone Photovoltaic System using Fuzzy Sliding Mode Control Maximum Power Point Tracking Control for Stand-Alone Photovoltaic System using Fuzzy Sliding Mode Control Antonius Rajagukguk, Mochamad Ashari, Dedet Candra Riawan
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PS12 The Influence of Meteorological Parameters under Tropical Condition on Electricity Demand Characteristic: Indonesia Case Study Yusri Syam Akil, Syafaruddin, Tajuddin Waris, A. A. Halik Lateko
381
PS13 Optimal Distribution Network Reconfiguration with Penetration of Distributed Energy Resources Ramadoni Syahputra, Imam Robandi, Mochamad Ashari
386
PS14 Maximum Power Point Tracking Photovoltaic Using Root Finding Modified Bisection Algorithm Soedibyo, Ciptian Weried Priananda, Muhammad Agil Haikal
392
PS15 Design of LLC Resonant Converter for Street Lamp Based On Photovoltaic Power Source Idreis Abdualgader , Eflita Yohana, Mochammad Facta
398
PS16 Power Loss Reduction Strategy of Distribution Network with Distributed Generator Integration Soedibyo, Mochamad Ashari, Ramadoni Syahputra
402
PS17 Double Dielectric Barrier Discharge Chamber for Ozone Generation Mochammad Facta, Hermawan, Karnoto,Zainal Salam, Zolkafle Buntat
407
PS18 Leakage Current Characteristics at Different Shed of Epoxy Resin Insulator under Rain Contaminants Abdul Syakur, Hermawan
411
PS19 Transformer monitoring using harmonic current based on wavelet transformation and probabilistic neural network (PNN) Imam Wahyudi F., Wisnu Kuntjoro Adi, Ardyono Priyadi, Margo Pujiantara, Mauridhi Hery P
417
TELECOMUNICATIONS
TE1 Data Rate of Connections Versus Packet Delivery of Wireless Mesh Network with Hybrid Wireless Mesh Protocol and Optimized Link State Routing Protocol Alexander William Setiawan Putra, Antonius Suhartomo
422
TE2 Empirical Studies of Wireless Sensor Network Energy Consumption for Designing RF Energy Harvesting Eva Yovita Dwi Utami, Deddy Susilo, Budihardja Murtianta
427
TE3 Modulation Performance in Wireless Avionics Intra Communications (WAIC) Muhammad Suryanegara, Naufan Raharya
432
TE4 Implementation and Performance Analysis of Alamouti Algorithm for MIMO 22—أ Using Wireless Open-Access Research Platform (WARP) Rizadi Sasmita Darwis, Suwadi, Wirawan, Endroyono, Titiek Suryani,
Prasetiyono Hari Mukti
436
TE5 Period Information Deviation on the Segmental Sinusoidal Model Florentinus Budi Setiawan
441
TE6 A Compact Dual-band Antenna Design using Meander-line Slots for WiMAX Application in Indonesia Prasetiyono Hari Mukti, Eko Setijadi, Nancy Ardelina
445
TE7 Design and Analysis of Dualband J-Pole Antenna with Variation in “T” Shape for Transceiver Radio Communication at VHF and UHF Band Yoga Krismawardana, Yuli Christyono, Munawar A. Riyadi
449
TE8 Low Cost Implementation for Synchronization in Distributed Multi Antenna Using USRP/GNU-Radio Savitri Galih, Marc Hoffmann, Thomas Kaiser
455
TE9 Development of the First Indonesian S-Band Radar Andrian Andaya Lestari,Oktanto Dedi Winarko, Herlinda Serliningtyas, Deni Yulian
459
Back Cover
MRC NN Controller for Arm Robot Manipulator
M. Khairudin
Electrical Engineering Education Dept.
Faculty of Engineering Universitas Negeri Yogyakarta
Yogyakarta
Nur Kholis
Electrical Engineering Education Dept.
Faculty of Engineering Universitas Negeri Yogyakarta
Yogyakarta
Abstract—This paper presents investigations into the
development of model reference control based on a neural
network (NN) for robot manipulator. A NN used as a controller
network and a plant model network. A dynamic model of the system is derived using a Lagrange-Euler. The controller to
simplify a nonlinearities problem that can be efficiently solved
using NN. To study the effectiveness of the controller, initially a
nonlinear model is developed for one link robot manipulator. The
performances of the NN controllers are assessed in terms of the input tracking controller capability of the system and
disturbance robustness. The input is generated by a combined
multiple steps input. Finally, a comparative assessment of the
input tracking control and a disturbance robustness is presented.
The results show that NN controller performs give increasing
profiles.
Keywords—model reference; NN; robot manipulator
I. INTRODUCTION
Robotics is a special engineering science which deals with robot design, modelling, controlling and utilization [1].
Manipulator robot dynamics has an affair with the mathematical formulations of the equations of robot arm
motion. The dynamic equations of manipulator robot motion consist a set of equations describing the dynamic behavior of
the manipulator. Such equations of motion are useful for
computer simulation of manipulator robot motion, to design of a suitable control for a manipualtor robot, and to evaluate the
kinematic design and structure of a manipulator robot.
The main goal of modelling of a manipulator robot is to
achieve an accurate model representing the actual system behaviour. It is important to recognise the dynamic
characteristics of the system and construct a suitable mathematical framework. Several approaches are available to
create a model of manipulator robot dynamics, such as the
Lagrange-Euler, the Newton-Euler, the recursive Lagrange-Euler, and the generalized d'Alembert principle formulations
[2].
Some researchers have used neural networks controllers
for nonlinear systems based on the identification of the plant, learning the dynamic of the system and training the neural
network controller. Narendra and Parthasarathy [3] present the
problem of control and identification of dynamical systems using neural networks with statics and dynamics feedforward
NN for SISO and MIMO systems extended to model reference
adaptive control (MRAC).
NN control model well known for the nonlinear auto
regressive moving average (NARMA) that the model is close representation pf the nonlinear model of equilibrium state [4].
Subudhi and Morris [5] have also presented a systematic
approach for deriving the dynamic equations for n-link manipulator where two-homogenous transformation matrices
are used to describe the rigid and flexible motions respectively.
In the learning controller, a dynamic recurrent neural network contains a state feedback and provides more
computational advantages than a back-propagation neural network and models the inverse dynamics of the manipulator
system. Gutierrez [6] proved that the tracking performance of
the NN controller is far better than that of the PD or PID standard controllers.
Input tracking performance has been objectived when using the intelligent control such as NN. Lewis et.al [7]
investigated standard NN backpropagation when used in the real-time closed-loop control will yield unbounded NN
weights with several requirments such as the net can not
exactly reconstruct a certain required control function or there are bounded unknown disturbances in the robot dynamics.
The adaptive neural network controller used online training algorithm based on the error dynamics , although
the neural networks are trained offline with a backpropagation algorithm [8]. The design and architecture of
the neural networks are explained along with the
identification procedure of the robotic system. Also compared between NN controller and PD controller to test
the performance of the neural network controller.
This paper mainly presents an investigation into the
dynamic modelling and model reference control using NN of a robot manipulator. It is found that the MRC NN controller for
a combined multiple steps input tracking has not been explored fr control of a robot manipulator. Simulation of the
dynamic model is performed in Matlab and Simulink. System
responses namely angular position is evaluated. Moreover, the works investigates the effects disturbance on the dynamic
characteristics of the system. The work presented forms the basis of design and development of suitable control strategies
2014 1st International Conference on Information Technology, Computer and Electrical Engineering (ICITACEE)
978-1-4799-6432-1/14/$31.00 ©2014 IEEE 39
for arm robot manipulator systems. The rest of the paper is
structured as follows a brief description and modelling of the arm robot manipulator system considered in this study.
Introduction of the model reference control using NN and the controller constraints taken into account. Simulation results
input tracking performance of the NN are presented.
II. ROBOT ARM MANIPULATOR
A. Dynamic and Kinematic
In this section, the arm manipulator kinematics is described. The physical parameters of the robot manipulator
system considered in this study are shown in Table 1.
T ABLE 1. PARAMETERS OF A ROBOT MANIPULATOR
Symbol Parameter Value Unit
ML Mass of link 0.05 kg
ρ Mass density 0.2 kgm-1
G Gear ratio 1 -
EI Flexural rigidity 1.0 Nm2
Jh Motor and hub inertia 0.02 kgm2
l Length 0.5
Mh Mass of the centre rotor 0.2 kg
The kinematics description is developed for a chain of connected rigid links as shown in Figure 1. The co-ordinate
systems of the link are assigned referring to the Denavit–Hartenberg (D–H) description. X0Y0 is the inertial co-ordinate
frame (CF), XiYi the rigid body CF associated with the ith link.
Fig. 1. Schematic of Manipulator Robot
The developed modelling based on an Euler-Lagrange
simulation algorithm characterising the dynamic behaviour of
the manipulator robot system. The description of kinematics is
developed for a chain of n serially connected flexible links.
To derive the dynamic equations of motion of a robot
manipulator, the total energies associated with the manipulator
system needs to be computed using the kinematics
formulations. L is lagrangian otherwise T dan U are the total
kinetic and potential energy of the manipulator respectively,
that in the cartesian axis are given by
)x(U)x(TL
(1)
where Tn321 ]x,..x,x,x[x
in order to use generalized coordinates with T
s321 ]q,..q,q,q[q where )n,...2,1i(x1 as a function of
q and ix is a function of q dan q .
Based on the Lagrangian equation (2) can be found
)q(U)q,q(TL
(2)
Otherwise for n-link robot manipulator, the kinetic energi can
be shown
ii
Tii
Tiii I
2
1vvm
2
1T (3)
Using kinematic equation for n-link, can be found
qJx
where TTvx , J , v and are Jacobian matric, linear
vector velocity and angular velocity respectively. If this is subtituted to equation (3) will be
q)q(Dq2
1T T (4)
where )q(D is inertia matric of n-link robot manipulator.
The total potential energy of the system due to the
deformation of the link i by neglecting the effects of the
gravity can be written as
oii
n
i
pm2
1U (5)
position vector oip is measured from robot normal position to
center of mass im .
To subtitute equation (4) and (5) then can be found
)q(pm2
1q)q(Dq
2
1L oii
n
i
T
given the differential equation
i11 q
L
q
L
dt
d
(6)
using partial differential equation (PDE) can be writen
Considering the damping, the desired dynamic equations of
motion of a robot manipulator can be obtained as
qgqq,qfq)q(M (7)
where f is the vectors containing terms due to coriolis
and centrifugal forces, M is the mass matrix and g is the
vectors containing terms due to the interactions of the link
angles and their rates with the modal displacements.
θ
X0
Link
Yi
Y0
Xi
40
B. Model Reference Control
The NN used as a controller network and a plant model
network. The neural model reference control schematic uses two neural networks. There are a controller network and a
plant model network, as shown in Figure 2. The plant model is
identified first, and then the controller is trained so that the plant output follows the reference model output.
Fig 2. Schematic of Model Reference Controller
III. NEURAL NETWORK FOR MRC AND CONTROLLER
The function NN are trained using a backpropagation
method and sigmoidal activation functions can be obtained as
e1
1
(8)
The dynamics of the robot will be learned by NN controller and then the controller output will be adjusted to
make a stabil of the robot motion. Several rules are adopted to make simplify before start the training process considering
the NN architecture, number of nodes or neurons and the
activation function.
Training of the controller used backpropagation method.
To minimise the weight function used function J . The
gradient of a performance function J can be obtained as
norm
Jnorm (9)
where norm and denote the nominal value of and
learning rate of NN respectively.
Figure 3 shows the simulation of MRC NN of robot manipulator model and the NN controller. For each NN has
two layers. Simulation using NN Toolbox Matlab [8]. There are three neurons that used for hidden layers. Also in this
simulation used three sets of controller inputs such as delayed reference inputs, delayed controller outputs and delayed plant
outputs.
Furthermore for each of inputs used number of delayed values. Typically, the number of delays will increase with the
order of the plant. There are two sets of inputs to the NN plant model such as delayed controller outputs and delayed plant
outputs.
IV. RESULTS AND DISCUSSION
Simulation of the developed dynamic model was implemented within the Matlab and Simulink environment on
Intel Pentium 1.86 GHz and 1.99 GB RAM. The system responses are monitored for duration of 50 s, and the results
are recorded with a sampling time of 10 ms. The angular position was obtained. For evaluation of the time response of
the angular position, settling time and overshoot of the
response are obtained. NN controller used for tracking performance.
In this study, the results of identification system using NN modelling can be shown at figure 4. The identification
process after the training procedure is shown. The results obtained good approximations when the controller is trained
with a small number of nodes or neurons in each
function of the neural controller. It is shown that the comparison of the output can track the input signal is more
similar although still have a minor mistake. In the system identification more suggested to keep the output signal can
follow the input signal.
Fig. 4. Comparison between input -output robot manipulator
Model
error +
- reference
Control
input
Control
error -
+
Plant
Output
Reference
model
NN Model
NN
Controller
Robot
Manipulator
41
The performance of identification of robot manipulator can be shown in Figure 5. The approximation result between
target and real output, the performance is 1.65x10-5
from the target is zero. It is noted that the comparison between the
target signal and real output is very closed similar.
Fig. 5. The approximation between target and real output
To check the input tracking capabilty of the NN controller
and identification model, a combined multiple steps input signals were used for the robot manipulator. In this study
given the combined multiple steps input with the step value of
5 rad can be shown in the Figure 6.
0 10 20 30 40 50-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
time
rad
Fig. 6. Input of robot manipulator
The disturbance is given for the robot manipulator to check
the robustness of the dynamic system. In this study the disturbance is given at 30s with pulse signal of -1 rad as
shown in Figure 7.
Step2
Step1
Step
Scope
Robot Manipulator
Plant Output
Reference
Control
Signal
Neural
Network
Controller
Model Reference Controller
Disturbance
Disturbance
Add2
Add
TorqueTorque
Angle
Fig. 3. The MRC NN of robot manipulator
42
0 10 20 30 40 50-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
time
rad
Fig. 7. Disturbance of robot manipulator
For the tracking input capabilty, the performance of NN controller can be obtained in Figure 8. In this study, the
controller is used for input tracking capability of the robot manipulator. The time response spesification is shown with
the settling time and overshoot are 4.10s and zero overshoot respectively. It is noted that the controller can track the given
input. Also the output system can show a stability from the disturbance that given at 30s. Figure 8 also show the output
system can achieve a steady state around 2s after get the
disturbance.
0 10 20 30 40 50-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
time
rad
output
input
Fig 8. The performance of NN controller for input tracking
V. CONCLUSSION
The development of MRC NN for robot manipulator has
been presented. A MRC NN controller has been implemented
for input tracking control of the robot manipulator. MRC NN
controller presented the performance of identification of robot
manipulator with a minor error approximation. Performances
of the control schemes have been evaluated in terms of the
multiple steps input tracking capability of the system with
disturbance robustnes. Simulations of the dynamic model and
NN control have been carried out in the time domains where
the system responses including angular positions are studied.
In term of input tracking and disturbance robustness , NN
controller has been shown to be an alternative technique.
Acknowledgment This research was supported by grant of Hibah Bersaing
of Universitas Negeri Yogyakarta, contract no. HB-
BOPTN/UN 34.21/2014. The authors would like to thank the anonymous reviewers for their precious suggestions for
this paper.
References [1] Mahmoud Gouasmi, Mohammed Ouali, Brahim Fernini, and M’hamed
Meghatria, Kinematic Modelling and Simulation.
[21 Spong, M.W, Vidyasagar M,” Robot dynamics and control,” John Wiley & Sons, 2008/8/4.
[3] Narendra, K.S, Parthasarathy, K, "Identification and control of dynamical systems using neural networks," Neural Networks, IEEE Transactions vol.1, no.1, pp.4-27, Mar 1990
[4] Narendra, K.S, Mukhopadhyay, S, "Adaptive control using neuralnetworks and approximate models," Neural Networks, IEEE Transactions, vol.8, no.3, pp.475-485, May 1997
[5] Subudhi B and Morris A. S, “Dynamic modelling, simulation and control of a manipulator with flexible links and joints”, Robotics and Autonomous System , Vol. 41, 257-270, 2002.
[6] Gutierrez, L. B. Lewis, F. L. and J. Andy Lowe, “ Implementation of a Neural Network Tracking Controller for a Single Flexible Link : Comparison with PD and PID Controllers”. IEEE Transaction on industrial electronics. 45(2): 307-318.1998.
[7] Lewis FL, Liu K, and Yesildirek A, “Neural net robot controller with guaranteed tracking performance”, IEEE Trans Neural Netw. 1995;6(3):703-15.
[8] Mathworks, Neural Network Toolbox, Matlab R2014a.
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