June 2020: Top Read Articles in Control Theory and Computer Modelling

June 2020: Top Read
Articles in Control Theory
and Computer Modelling
International Journal of Control Theory and
Computer Modelling (IJCTCM)
Google Scholar
ISSN : 2249-1155 [Online]; 2319 - 4138 [print].
http://airccse.org/journal/ijctcm/ijctcm.html

ZIGBEE: A LOW POWER WIRELESS TECHNOLOGY FOR
INDUSTRIAL APPLICATIONS
Nisha Ashok Somani1
and Yask Patel2
1
Department of Computer Science & Engg Parul Institute of Engineering and technology,At
post Limda Waghodia
2
Informaion & Technology Department Parul Institute of Engineering and technology,At post
Limda Waghodia
ABSTRACT:
The great potential of Wireless Sensor Network is being seen in industrial, consumer and
commercial application. The wireless technology is becoming one of the most prominent areas of
research. This paper focuses on the most widely used transceiver standard in Wireless Sensor
Networks, a ZigBee technology. ZigBee over IEEE 802.15.4 defines specifications for low data rate
WPAN (LR-WPAN) to support low power monitoring and controlling devices. This paper presents a
Zigbee wireless standard, IEEE 802.15.4specification, ZigBee device types, the protocol stack
architecture and its applications.
KEYWORDS:
ZigBee, IEEE802.15.4
For More Details: http://airccse.org/journal/ijctcm/papers/2312ijctcm03.pdf
Volume Link: http://airccse.org/journal/ijctcm/vol2.html

REFRENCES
[1] ZigBee Alliance, ZigBee Specification[z]. Version 1.0, http://www.ZigBee.org, 2005-
06-27
[2] ShizhuangLin; JingyuLiu; YanjunFang; Wuhan Univ., Wuhan" ZigBee Based Wireless
SensorNetworks and Its Applications in Industrial”IEEE International Conference on
Automation and Logistics, 200718-21Aug.2007page(s):1979-1983Location:Jinan
[3] Zhou Yiming, Yang Xianglong, Guo Xishan, Zhou Mingang, Wang Liren ,” A Design
of Greenhouse Monitoring & Control System Based on ZigBee Wireless Sensor
Network”,IEEE journa1-4244-1312-5/07 2007
[4] R Vishnubhotla, PS Rao, A Ladha, S Kadiyala, A Narmada, B Ronanki, S
Illapakurthi,”ZigBee Based Multi-Level Parking Vacancy Monitoring System” 978-1-
4244-6875-10/2010 IEEE pg 2563-2566
[5] Xiuping Zhang; Guangjie Han; Changping Zhu; Yan Dou; Jianfeng Tao;” Research of
Wireless Sensor Networks based on ZigBee for Miner Position”, [J] International
Symposium on Computer, Communication, Control and Automation, IEEE. 29 July
2010Pg1 – 5
[6] Dunfan Ye, Daoli Gong, Wei Wang,“Application of Wireless Sensor Networks in
Environmental Monitoring”2nd International Conference on Power Electronics and
Intelligent Transportation SystemIEEE2009pg 2563-2567
[7] ShizhuangLin; JingyuLiu; YanjunFang; Wuhan Univ.,Wuhan” ZigBee Based Wireless
Sensor Networks and Its Applications in Industrial” ,IEEE International Conference on
Automation and Logistics18-21Aug.2007Pg1979-1983

A Time Series ANN Approach for Weather Forecasting
Neeraj Kumar1
, Govind Kumar Jha2
1
Associate Professor and Head Deptt. Of Computer Science ,Nalanda College Of
Engineering Chandi(Bihar)
2
Assistant Professor, Deptt. Of Computer Engineering, GLA University, Mathura(UP),
India
Abstract:
Weather forecasting is most challenging problem around the world. There are various
reason because of its experimented values in meteorology, but it is also a typical unbiased
time series forecasting problem in scientific research. A lots of methods proposed by various
scientists. The motive behind research is to predict more accurate. This paper contribute the
same using artificial neural network (ANN) and simulated in MATLAB to predict two
important weather parameters i.e. maximum and minimum temperature. The model has been
trained using past 60 years of real data collected from(1901-1960) and tested over 40 years
to forecast maximum and minimum temperature. The results based on mean square error
function (MSE) confirm, this model which is based on multilayer perceptron has the
potential to successful application to weather forecasting.
Keywords:
Artificial neural network, Multilayer perceptron, Time series analysis, Mean Square error
function, MATLAB.

REFERENCES:
1. Paras, S.Mathur, A.Kumar and M.Chandra (2007), "A Feature Based Neural Network
Model for Weather Forecasting",World Academy of Science, Engineering and
Technology.
2. Mr.R.C vashishtha, director IMD.
3. S N Sivanandam, S Sumathi, S N Deepa, "Introduction to neural Networks using
MATLAB".
4. http://en.wikipedia.org/wiki/Neural_network.
5. Rosenblatt, Frank. x.(1961),"Principles of Neurodynamics: Perceptrons and the Theory
of Brain Mechanisms", Spartan Books, Washington DC.
6. Rumelhart, David E., Geoffrey E. Hinton, and R. J. Williams (1986),“Learning Internal
Representations by Error Propagation”. David E. Rumelhart, James L. McClelland, and
the PDP research group. (editors), Parallel distributed processing: Explorations in the
microstructure of cognition, Volume 1: Foundations. MIT Press.
7. Cybenko, G. (1989), "Approximation by superpositions of a sigmoidal function
Mathematics of control, Signals, and Systems (MCSS)", 2(4), 303–314.
8. http://en.wikipedia.org/wiki/Multilayer_perceptron.
9. Ramasubramanian V."Time Series Analysis", I.A.S.R.I,Library Avenue,New Delhi.
10. http://www.tropmet.res.in.
11. http://www.imd.gov.in.
12. Chattopadhyay S. (2006), “Multilayered feed forward Artificial Neural Network model
to predict the average summer-monsoon rainfall in India”,IEEE ,Vol.:11, pp.:125-130
13. Zan C. (2009) “Myanmar Rainfall Forecasting Using Hidden Markov Model”,IEEE
International Advance Computing Conference.
14. Hayati M. and Mohebi Z.(2007) , “Temperature Forecasting Based on neural Network
Approach”, World Applied Sciences Journal 2 (6):613-620.ISSN 1818-4952.

TAKAGI-SUGENO MODEL FOR QUADROTOR MODELLING AND CONTROL
USING NONLINEAR STATE FEEDBACK CONTROLLER
Fouad Yacef1
, Omar Bouhali1
, Hicham Khebbache2
and Fares Boudjema3
1
Automatic Laboratory of Jijel (LAJ), Automatic Control Department, Jijel University,
ALGERIA
2
Automatic Laboratory of Setif (LAS), Electrical Engineering Department, Setif University,
ALGERIA
3
Control Process Laboratory (LCP), National Polytechnic School (ENP), ALGERIA
ABSTRACT
In this paper we present a Takagi-Sugeno (T-S) model for Quadrotor modelling. This model is
developed using multiple model approach, composed of three locally accurate models valid in
different region of the operating space. It enables us to model the global nonlinear system with some
degree of accuracy. Once the T-S model has been defined it is claimed to be relatively
straightforward to design a controller with the same strategy of T-S model. A nonlinear state
feedback controller based on Linear Matrix Inequality (LMI), and PDC technique with pole
placement constraint is synthesized. The requirements of stability and poleplacement in LMI region
are formulated based on the Lyapunov direct method. By recasting these constraints into LMIs, we
formulate an LMI feasibility problem for the design of the nonlinear state feedback controller. This
controller is applied to a nonlinear Quadrotor system, which is one of the most complex flying
systems that exist. A comparative study between controller with stability constraints and controller
with pole placement constrains is made. Simulation results show that the controller with pole
placement constrains yields good tracking performance. The designed T-S model is validated using
Matlab Simulink.
KEYWORDS
Linear Matrix Inequality (LMI), Multiple Model Approach (MMA), Parallel Disturbance
Compensation (PDC), Pole Placement, Quadrotor, Takagi-Sugeno model.

REFERENCES
[1] J. Novák. (2007) “linear system identification and control using local model networks”.
Doctorat Thesis, Faculty of Applied Informatics, Tomas Bata University, Zlín.
[2] T. Takagi & M. Sugeno, (1985) "Fuzzy identification of systems and its applications to
model and control", IEEE Transactions on Systems, Man, and Cybernetics, vol. 15, pp.
116–132.
[3] H. O. Wang, K. Tanaka, & M. Griffin, (1996) "An approache to fuzzy controle of non
linear systems : stability and design issues," IEEE Transaction on fuzzy system, vol. 4, pp.
14-23.
[4] K. Tanaka, . T. Ikeda, & Y. Y. He, (1998) "Fuzzy regulators and fuzzy observers : relaxed
stability conditions and LMI-based design," IEEE Transaction on fuzzy system, vol. 6, pp.
250-256.
[5] S. Boyd, L. El Ghaoui, E. Feron, & V. Balakrishnan (1994) "Linear Matrix Inequalities in
System and Control Theory," SIAM, Philadelphia, USA.
[6] T. Madani & A. Benallegue, (2006) "Backstepping Sliding Mode Control Applied to a
Miniature Quadrotor Flying Robot", IEEE Conference on Industrial Electronics, pp. 700-
705.
[7] Y. Yu, J. Changhong, & W. Haiwei, (2010) "Backstepping control of each channel for a
quadrotor aerial robot", International Conference on Computer, Macaronis, Control and
Electronic Engineering (CMCE), pp. 403-407.
[8] S. Bouabdallah, A. Noth, & R. Siegwart, (2004) "PID vs LQ control techniques applied to
an indoor micro quadrotor", IEEE International Conference on Intelligent Robots and
Systems, Sendal, Japan, pp. 2451-2456.
[9] K. Alexis, G. Nikolakopoulos, & A. Tzes, (2010) "Constrained-control of a quadrotor
helicopter for trajectory tracking under wind-gust disturbances", IEEE Mediterranean
Electro-technical Conference (MELECON), pp. 1411-1416.
[10] H .Bouadi, S. S. Cunha, A. Drouin, & F. M. Camino, (2011) "Adaptive Sliding Mode
Control for Quadrotor Attitude Stabilization and Altitude Tracking", IEEE International
Symposium on Computational Intelligence and Informatics, pp. 449-455.
[11] R. Gao & A. O'Dwyer, (2002) "Multiple model networks in non-linear system modelling
for control–a review", in the 3nd Wismar Automates rungs symposium, Wismar,
Germany.
[12] F. Yacef & F. Boudjema, (2011) "Local Model Network for non linear modelling and
control of an UAV Quadrotor" ", International Conference on Automatic and Macaronis
(CIAM), Oran, Algeria, pp. 247-252.

ROBUST SECOND ORDER SLIDING MODE CONTROL FOR A
QUADROTOR CONSIDERING MOTOR DYNAMICS
Nader Jamali Soufi Amlashi1
, Mohammad Rezaei 2
, Hossein Bolandi2 and
Ali Khaki Sedigh3
1
Department of Control Engineering, Malek Ashtar University of Technology, Tehran,
Iran 2
Department of Control Engineering,Iran University of Science and
Technology,Tehran, Iran 3
Department of Control Engineering, Khaje Nasir Toosi
University of Technology, Tehran, Iran
ABSTRACT
In this paper, a robust second order sliding mode control (SMC) for controlling a quadrotor with
uncertain parameters presented based on high order sliding mode control (HOSMC). A controller
based on the HOSMC technique is designed for trajectory tracking of a quadrotor helicopter with
considering motor dynamics. The main subsystems of quadrotor (i.e. position and attitude) stabilized
using HOSMC method. The performance and effectiveness of the proposed controller are tested in a
simulation study taking into account external disturbances with consider to motor dynamics.
Simulation results show that the proposed controller eliminates the disturbance effect on the position
and attitude subsystems efficiency that can be used in real time applications.
KEYWORDS
Quadrotor, High order sliding mode, Motor dynamics

REFERENCES
[1] S. Bouabdallah, P. Murrieri and R. Siegwart, (2004) “Design and control of an indoor
micro quadrotor,” IEEE International Conference on Robotics and Automation (ICRA
’04), Vol.5, pp. 4393 - 4398.
[2] P. Pounds, R. Mahony and P. Corke, (2006) “Modelling and Control of a Quad-Rotor
Robot,” In the Proceedings of the Australasian Conference on Robotics and
Automation.
[3] O. Fritsch, P. De Monte, M. Buhl and B. Lohmann, (2012) “Quasi-static feedback
linearization for the translational dynamics of a quadrotor helicopter,” American
Control Conference (ACC), 2012, pp. 125 – 130.
[4] A. A. Mian and W. Daobo, (2008) “Modelling and Backstepping-based Nonlinear
Control Strategy for a 6 DOF Quadrotor Helicopter,” Chinese Journal of Aeronautics,
Volume 21, Issue 3, pp. 261– 268.
[5] A. Das, F. Lewis and K. Subbarao, (2009) “Backstepping Approach for Controlling a
Quadrotor Using Lagrange Form Dynamics,” Journal of Intelligent and Robotic
Systems, Volume 56, Issue 1-2, pp. 127-151.
[6] R. Zhang, X. Wang and K. Y. Cai, (2009) “Quadrotor aircraft control without velocity
measurements,” IEEE Conference on Decision and Control, pp. 5213 – 5218.
[7] Y. Morel and A. Leonessa, (2006) “Direct Adaptive Tracking Control of Quadrotor
Aerial Vehicles,” ASME International Mechanical Engineering Congress and
Exposition. American Society of Mechanical Engineers, pp. 155-161.
[8] H. Haomiao, G. M. Hoffmann, S. L. Waslander, and C. J. Tomlin, (2009)
“Aerodynamics and control of autonomous quadrotor helicopters in aggressive
maneuvering,” IEEE International Conference on Robotics and Automation, ICRA'09,
pp. 3277-3282.
[9] R. Guilherme, V. Manuel, G. Ortega and F. R. Rubio, (2010) “An integral
predictive/nonlinear H∞ control structure for a quadrotor helicopter,” Automatica, no.
1, pp. 29-39.
[10] L. Fridman, A. Poznyak and F. J. Bejarano, (2004) “Decomposition of the Mini-Max
Multimodel Optimal Problem via Integral Sliding Mode Control,” Proceedings of the
American Control Conference, vol.1, pp. 620 – 625.
[11] L. Luque-Vega, B. Castillo-Toledo, Alexander G. Loukianov, (2012) “Robust block
second order sliding mode control for a quadrotor,” Journal of the Franklin Institute
349, pp. 719–739.

[12] A. Mokhtari and A. Benallegue, (2003) “Dynamic feedback controller of Euler angles
and wind parameters estimation for a quadrotor unmanned aerial vehicle,” American
Control Conference (ACC’03), Denver, June.
[13] A. G. Loukianov, (2002) “Robust block decomposition sliding mode control design,”
International Journal Mathematical Problems in Engineering: Theory, Methods and
Applications, pp. 349–365.
[14] S. Mondal, C. Mahanta, (2013) “Adaptive integral higher order sliding mode controller
for uncertain systems,” Journal of Control Theory and Applications, Volume 11, Issue
1, pp. 61-68.
[15] Y. B. Shtessel, I. A Shkolnikov and M. D. J. Brown, (2003) “An asymptotic second-
order smooth sliding mode control,” Asian Journal of Control, pp. 498–504.
[16] L. Derafa, L. Fridman, A. Benallegue and A. Ouldali, (2010) “Super twisting control
algorithm for the four rotors helicopter attitude tracking problem,” In Proceedings of
11th International Workshop on Variable Structure Systems (VSS) Workshop.
[17] S. Bouabdallah and R. Siegwart, (2005) “Backstepping and sliding-mode techniques
applied to an indoor micro quadrotor,” In Proceedings of IEEE International
Conference on Robotics and Automation (ICRA ’05), pp. 2247–2252.
[18] L. Fridman and A. Levant, (1996) “Sliding Modes of Higher Order as A Natural
Phenomenon in Control Theory,” In Garofalo F., Glielmo L. (Eds.) Robust Control via
Variable Structure and Lyapunov Techniques, Lecture Notes in Control and
Information Sciences 217, Springer Verlag, pp. 107-133.
[19] J. P. Ostrowski and C. J. Taylor, (2005) “Control of a quadrotor helicopter using dual
camera visual feedback,” International Journal of Robotics Research, Vol. 24, No. 5,
pp. 329-341.
[20] S. Bouabdallah and R. Siegwart, (2005) “Backstepping and sliding-mode techniques
applied to an indoor micro quadrotor,” In Proceedings of the IEEE International
Conference on Robotics and Automation, pp. 2259-2264.
[21] H. Bolandi, M. Rezaei, R. Mohsenipour, H. Nemati, S. M. Smailzadeh, (2013)
“Attitude Control of a Quadrotor with Optimized PID Controller,” Intelligent Control
and Automation, Vol.4, No.3, pp. 335- 342.
[22] Tommaso Bresciani, (2008) “Modelling, Identification and Control of a Quadrotor
Helicopter,” Master Thesis, Department of Automatic Control, Lund University.

[23] M. Wierema, (2008) “Design, implementation and flight test of indoor navigation and
control system for a quadrotor UAV,” Master Thesis, Faculty of Aerospace
Engineering, Delft University of Technology.
[24] D. A. Mercado1, R. Castro1 and R. Lozano, (2013) “Quadrotors Flight Formation
Control Using a Leader-Follower Approach,” European Control Conference (ECC
July 17-19, 2013, Zurich, Switzerland), pp. 3858-3863.
[25] Hassan K. Khalil, (2002) “Nonlinear Systems,3rd Edition, Prentice Hall, ISBN 0-13-
067389-7.
[26] R. Xu and U. Ozguner, (2006) “Sliding Mode Control of a Quadrotor Helicopter,”
Proceedings of the 45th IEEE Conference on Decision & Control, San Diego, CA,
USA, pp. 4957- 4962.
Authors
Nader Jamali Soufi Amlashi
He was born in Guilan, Iran on January 1988. He received the B.S. degree in
Electrical Engineering from Tabriz University, Tabriz, Iran, in 2011. He obtained
his M. Tech. in control engineering from M.U.T University, Tehran, Iran in 2013.
His research interests include applications of nonlinear modelling, identification,
control, design nonlinear observers based on high order sliding mode and
implementation of autopilot for Unmanned Aerial Vehicle (UAV) such as
Quadrotors.
Mohammad Rezaei
He was born in Tehran, Iran on Aug.1963. He received the B.S. degree in Electrical
Engineering from Isfahan University, Isfahan, Iran, in 1990 and M. Tech. in control
engineering from K. N. Toosi University of Technology, Tehran, Iran, in 2000. He is
pursuing his PhD. in control engineering from Iran University of Science and
Technology, Tehran, Iran. His research interests include applications of nonlinear
control, design and implementation of Flying Robot.
Hossein Bolandi
He received his D.Sc. degree in electrical engineering from George Washington University,
Washington, D.C., in 1990.Since 1990 he has been with the College of Electrical
Engineering, Iran University of Science and Technology, Tehran, Iran, where he is
an associate professor. His research interests are in attitude determination and
control subsystems of satellites, robotics a nd adaptive control.Dr. Bolandi is the
author of 25 journal papers an d 105 papers in the international and Iranian
conference proceedings.

Ali Khaki Sedigh
He is currently a professor of control systems with the Department of Electrical and Computer
Engineering, K. N. Toosi University of Technology, Tehran, Iran. He obtained an honors degree in
mathematics in 1983, a master's degree in control systems in 1985 and a PhD in
control systems in 1988, all in the UK. He is the author and co-author of about 90
journal papers, 170 international conference papers and has published 14 books in the
area of control systems. His main research interests are adaptive and robust
multivariable control systems, complex systems and chaos control, research ethics and
the history of control.

AN EFFICIENT IMPLEMENTATION FOR KEY MANAGEMENT
TECHNIQUE USING SMART CARD AND ECIES CRYPTOGRAPHY
Neha gupta1
and Harsh Kumar Singh2
and Anurag jain3
1,2,3
Department of Computer Science, RITS Bhopal, M.P(India)
ABSTRACT
A Elliptic curve cryptosystem are become popular because of the reduced number of keys
bits required in Comparision to other cryptosystem. In existing work ECC technique are
used to describe the encryption data to provide a security over a network. ECC satisfy the
Smart cards requirements in term of memory, processing and cost. In existing work ECC
cryptographic Algorithm work with a smart card technique. Many existing approaches work
with smart card with various Technique and produce a better efficient result. In these review
paper, we Define a smart card technique using a ECIES cryptographic algorithm. So These
Technique key management using smart card and ECIES.ECC basically based on a discrete
logarithm over appoint on an elliptic curve. The ECIES is standard elliptic curve that is
totally based on encryption algorithm. Smart Card using ECIES technique in key
management technique.
KEYWORDS
ECIES, elliptic curve, encryption, Decryption, public key cryptography, Smart card, java
card.

REFERENCES
[1] Swarn Sanjay Sonwanshi,Ram ratan ahirwal,Yogendra kumar jain”An efficient smart
card based remote user authentication scheme using hash function”,IEEE ,conferences
on electrical,electronics and computer science,2012.
[2] Patrick george “user authentication with smart card in trusted computing
architecture”Gemplus.
[3] A.k.awasthi and S.lal,”A remote user authentication scheme using smart card with
forward security”,IEEE transactions on consumer Electronics,Vol.49,.no.4,pp,1246-
1248,2003.
[4] B.Baker “Mutual Authentication with smart card”Delft university of
technology,Chicago,Illinois,USA,may 10-11-1999.
[5] R. ramasamy and Amutha prabakar Muniyandi”An efficient password authentication
scheme for smart card”,international journal of network security ,vol.14,no.3,pp 180-
186,may 2012.
[6] R. ramasamy and Amutha prabakar Muniyandi ”new remote Mutual Authentication
scheme using smart card” transaction on data Privacy 2(2009) 141-152.
[7] C. K. Chan and L. M. Cheng, “Cryptanalysis of a remote user authentication scheme
using smart cards,” IEEE Trans. Consumer Electron., vol. 46, pp. 992-993, 2000.
[8] C. C. Chang and S. J. Hwang, “Using smart cards to authenticate remote passwords,”
Computers and athematics with applications, vol. 26, No.7, pp. 19-27, 1993.
[9] C. C. Chang and K. F. Hwang, “Some forgery attack on a remote user authentication
scheme using smart cards,” Infomatics, vol. 14, no. 3, pp.189 - 294, 2003.
[10] C. C. Chang and T. C. Wu, “Remote password authentication with smart cards,” IEE
Proceedings-E, vol. 138, no. 3, pp. 165-168, 1993.

AUTHORS
Neha Gupta received the B.E degree in Computer science from RGPV
university, Bhopal, India in 2009,And the M.TECH pursing in Computer
science & engineering From RITS, RGPV, Bhopa l.
Harsh Kumar Singh received the B.E degree in information technology from
RGPV university, Bhopal, India, In 2006, and the M.TECH degree in
computer science & engineering from MANIT university, Bhopal, India in
2011.He is a currently a Asst. Prof. in RITS Bhopal, India. His current
research interests include wireless sensor network, time synchronization.
Mr.Anurag Jain is a associate Professor and head of the department of CSE
in Radharaman institute of technology & science Bhopal.He is also the dean
academics RITS. Anurag Jain has completcded his M.tech(IT),He is also
pursing Phd.in RGPV university. He is 12 year teaching experience and area
of interest including network security. TOC, data structure, OOPs, Basic
computer, Complier design. He is organized and attend several national and international
conferences.He also publishe d 36 international journal paper and 2 national paper. He is
also published a book of basic computer engineering.He is life member of CSI(Computer
socity of india).

June 2020: Top Read Articles in Control Theory and Computer Modelling

More Related Content

What's hot (20)

Similar to June 2020: Top Read Articles in Control Theory and Computer Modelling (20)

Recently uploaded (20)

June 2020: Top Read Articles in Control Theory and Computer Modelling