Page 1 of 40 Biography of DR. Muhammad Shafiq Dr. Muhammad Shafiq is currently working as an Associate Professor in Department of Electrical and Computer Engineering at Sultan Qaboos University (SQU), Oman. Prior to this, he has worked as a faculty member for 20 years in four Universities. He has been technical manager in Saudi Technical Engineering Associates (French J Thomson) for one year. He is the founder principal of Rawalpindi Institute of Technology and has worked as a faculty member at Pakistan Institute of Engineering and Applied Science (PIASE) for two years. He has worked in a reputed institute, King Fahad university of petroleum and minerals (KFUPM) for eight years as a faculty member in Systems Engineering Department. He has also served in Ghulam Ishaque Khan Institute of Engineering, Sciences and Technology (GIKI) and International Islamic university, Islamabad (IIUI) Pakistan for three years as Professor of Automation and Control. He was founder principal of Rawalpindi Institute of Tchnology (RIT), Pakistan. Over the years, Dr. Shafiq has taught seven graduate courses in intelligent control, automation and mobile robotics. He has taught 22 undergraduate courses in the area of electrical and mechatronics engineering. He has developed control and automation Labs at KFUPM, GIKI and SQU. He has conducted several short courses in the area of industrial process control and programmable logic control systems. Dr. Shafiq has been the convener and member of several curriculum development committees. He has served as a member of ABET accreditation committee for Mechatronics Engineering program and Electrical and Computer Engineering program at SQU. He has also worked for the Pakistan engineering council as a member of the accreditation review committee for Electrical and Mechatronics Engineering programs. He has served as a member of national curriculum committee for BS program in communication systems under higher education commission, Pakistan. Dr. Shafiq research interests are in control systems, mechatronics and robotics. His recent research interests are in intelligent control of systems in science and engineering. He has authored more than 100 journal and conference papers in his area of interest. He has done collaborative research with researchers of international repute. His research has been supported by several research funding Organizations in Saudi Arabia, Pakistan and Oman. He has supervised nine master thesis and two doctorate thesis. He was member of several PhD and Master examination committees. In recognition of his teaching, research and academic services, he has been awarded several certificates and shields. He is a senior member of IEEE. Research Profile Cites: Please click the tabs to see the research profiles. H-index Citation Articles Scopus 9 295 63 Google Scholar 11 417 95 Research Gate 10 314 81
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Page 1 of 40
Biography of DR. Muhammad Shafiq
Dr. Muhammad Shafiq is currently working as an Associate Professor in Department of
Electrical and Computer Engineering at Sultan Qaboos University (SQU), Oman. Prior to this,
he has worked as a faculty member for 20 years in four Universities. He has been technical
manager in Saudi Technical Engineering Associates (French J Thomson) for one year. He is
the founder principal of Rawalpindi Institute of Technology and has worked as a faculty
member at Pakistan Institute of Engineering and Applied Science (PIASE) for two years. He
has worked in a reputed institute, King Fahad university of petroleum and minerals (KFUPM)
for eight years as a faculty member in Systems Engineering Department. He has also served
in Ghulam Ishaque Khan Institute of Engineering, Sciences and Technology (GIKI) and
International Islamic university, Islamabad (IIUI) Pakistan for three years as Professor of
Automation and Control. He was founder principal of Rawalpindi Institute of Tchnology (RIT),
Pakistan.
Over the years, Dr. Shafiq has taught seven graduate courses in intelligent control,
automation and mobile robotics. He has taught 22 undergraduate courses in the area of
electrical and mechatronics engineering. He has developed control and automation Labs
at KFUPM, GIKI and SQU. He has conducted several short courses in the area of industrial
process control and programmable logic control systems.
Dr. Shafiq has been the convener and member of several curriculum development
committees. He has served as a member of ABET accreditation committee for
Mechatronics Engineering program and Electrical and Computer Engineering program at
SQU. He has also worked for the Pakistan engineering council as a member of the
accreditation review committee for Electrical and Mechatronics Engineering programs. He
has served as a member of national curriculum committee for BS program in
communication systems under higher education commission, Pakistan.
Dr. Shafiq research interests are in control systems, mechatronics and robotics. His recent
research interests are in intelligent control of systems in science and engineering. He has
authored more than 100 journal and conference papers in his area of interest. He has
done collaborative research with researchers of international repute. His research has been
supported by several research funding Organizations in Saudi Arabia, Pakistan and Oman.
He has supervised nine master thesis and two doctorate thesis. He was member of several
PhD and Master examination committees. In recognition of his teaching, research and
academic services, he has been awarded several certificates and shields. He is a senior
member of IEEE.
Research Profile Cites: Please click the tabs to see the research profiles.
My vision of the teaching is to educate the students, who should become
responsible members of the society. Their professional activities should develop
a better future civilization in terms of ethical values and daily life facilities.
Engineering teaching methodology can be divided into three main categories
i.e. undergraduate, master and doctorate. In the undergraduate studies the
basic emphasis is mostly on preparing the students to work and communicate
efficiently in the industry as engineering professional. The master level education
targets to develop the graduates, who can work effectively in research and
development (R&D) teams. The doctorate level education goal is to produce
personals with abilities to carry out individual research, apply the knowledge in
transdisciplinary environment and contribute to the knowledge in the area of
interest. There is a strong link between the course objectives and the education
level goals. The outcomes of the engineering courses are associated with the
natural objectives of the courses and levels. Working as an engineering educationist, I understand that
a. A professional engineer should be in general able to
1. use the fundamental science principles in combination with
modern engineering tools and methods to solve problems
2. use engineering principles to conceptualize, create, model, test,
and evaluate designs within a context of local and global needs
3. work efficiently as a member of multidisciplinary teams and
communicate effectively
4. understand evolving technical, business, and societal issues as well as
his ethical responsibilities that impact his engineering profession and
the welfare of others
b. A master degree holder in engineering should be able to
1. effectively communicate with the R&D team
2. effectively use the engineering knowledge to accomplish the R&D tasks
3. timely finish the assigned R&D work
c. A doctor of philosophy in engineering should be able to
1. effectively communicate with a research team in a leading manner
2. perform research individually and can determine and arrange all
the research requirements for the area of interest
3. Obtain research funding
4. Propose solutions for the industry and society
5. timely finish the research work
The common teaching methodology for all levels should have at least the following
components,
1. Course Description
A clear description of the course objectives and the student
outcomes should be given to the students in the begging of the course.
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2. Structured Hierarchy of the Content
Though a clear organization in the delivery material should be described
for the students, yet there should be a certain level of opportunity for the
students to arrange their own structuring, which suites for their learning
habits. Contents should include concepts, applications and problem
solving. A timeline of the delivery and the evaluation process should be
transparent to all the participants.
3. Use of images, videos and online resources
A large number of people admit that colorful pictorial description of the
material help them in understanding the concepts easily. Recorded
lectures and other related videos provide means for the students to learn
the subject according to their own pace. The use of internet and other
material provide now a days an easy access to information. A provision of
limited domain of online material to the students enhances their learning
ability. Unsupervised use of online material sometimes affects adversely.
4. Student activity
a. Students should be involved in the learning process. This activity
depends on the level of the class that a, b and c. a can be further
subdivided into three level2, level3 and level4. The freshman level1 is
not included because mostly science courses are taught at this level in
the engineering curriculums.
Level2
Forming teams in the class with mix levels of students and assigning
group leaders. Group tasks are assigned for solving the problems. In
the class less demanding problems should be assigned while open
ended analytical problems should be given as homework. The aim of
this activity is to develop analytical ability based on scientific and
mathematical principles and methods. The groups should be given
time to share their solution with the class. Simple design problem may
be assigned at this level. The assessment of this activity normally poses
difficulties which can be partially addressed with the help of teaching
assistants. Well described laboratory based experiments and report
writing definitely improve the understanding level of the students.
Contemporary issues related to the course can be introduced using
reading assignments and group seminars.
Level3
At this level the development of design ability should be targeted. The
group should be assigned design problems. Open ended design
problems should be given as homework. In the laboratory
experiments students should be engaged in designing and
implementation of experiments for a given set of specifications. This
activity should have pre and post lab activities. If possible the groups
should be asked to give seminars on some of their work. The concept
of the tradeoffs and the technological limits in the design should be
introduced.
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Level4
Industrial case studies and introduction to industrial standards should
be main target of these courses. Team term projects and industrial
visits are helpful in explaining these topics to the students. Life learning
ability can be developed through open ended problems
assignments.
b. At the master level courses state of the art concepts of the subject
should be introduced. The fine details should be discussed. Term
projects related to recent works in the area of study help in improving
the understanding. Group homework assignment and seminars on the
term projects based on contemporary issues can be used for the
development of research and communication skills.
c. At the doctor level studies the courses should be designed to discuss
the state of the art research topic in the subject area. Research
oriented homework and term projects can enhance the learning
performance at this stage as well. Fine details of the topic should be
included in the course.
5. Timely Feedback
Feedback should be given timely and, if at all possible, positive. Reward
is much better than punishment. Students should be given a second
chance to practice after feedback in order to benefit fully from it.
6. Positive Attitude of the Professor
Positive expectations by the professor and respect from the professor are
highly motivating factor for the students. Low expectations and disrespect
are demotivating. This is a very important principle, but it cannot be
learned as a “method.” A good teacher believes that his or her students
are capable of great things.
7. Motivation by Success Oriented Challenges
Assigning challenging problems having high probability of success to the
students provide mean for increasing the motivation. Preparation of such
problems is a challenge for the professors. It requires a good knowledge of
the student background. A sufficient time for completion of tasks should
be given to the students, so that most of them should successfully finish the
assignment. Beside this, there should be a challenge for all the participants.
Success is very motivating. Further, thought-provoking open ended
questions can be used to improve the motivation. Posing questions without
answers can be particularly motivating for more mature students.
8. Encouraging Students to Teach
In a cooperative class naturally groups are formed. Students in the group
have better knowledge of the learning capabilities and approach of the
colleagues. Therefore, tutoring by the students in the groups improves
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learning ability of both the teacher and student. Moreover, tutors develop
a sense of accomplishment and confidence in their ability.
In brief, a clearly described course outline, team oriented activity based teaching,
cooperative and respectful class environment, group discussions, student
involvement in the teaching process and timely reward based success oriented
challenging assessment of the student’s outcomes significantly improve the learning
performance of a class.
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1.9 Research Statement
Research Summary
I started my research career by studying the problem of adaptive and robust
tracking of noninvertible linear dynamic systems, while I was working on my PhD
dissertation. I opt then the adaptive tracking of noninvertible systems as my main
area of research. Together with my research team, we developed several
algorithms for the adaptive tracking of applied systems. Our research focus is both
on theoretical and experimental developments. The adaptive control is an
interdisciplinary area of study in engineering and science. It gave me opportunities
to work with the other researchers in finding solutions of problems in the areas of
wireless sensor network, cellular mobile data analysis, gamma ray peak detection
and modeling of the water treatment systems. My co-researchers classify my
research work as interdisciplinary and transdisciplinary in nature. Our research
contributions have been published in reputed journals, conference proceedings,
book chapters and research reports. In the following discussion, I briefly describe
the contribution of our research work. A. Adaptive and Robust Tracking of Noninvertible Linear Dynamic Systems [1-10]
The main contributions of this work are
1. Development of approximated inverse systems using 2-Norm minimization
criterion based on adaptive finite impulse response filters
2. Development of linear phase approximate inverse systems using 2-Norm
minimization criterion approximation based on adaptive finite impulse
response filters
3. Development of all-pass approximation using 1-Norm criterion minimization
based on adaptive infinite impulse response filters
4. Development of efficient approximate inverse system using 2-Norm criterion
minimization based on adaptive finite impulse response filters
The stability and convergence proofs of the closed-loops are established using the
theory of linear adaptive control systems. The results are verified using the computer
simulation examples.
B. Adaptive Internal Model Control [12-13]
The main contribution this works are
1. Development of the adaptive internal model control strategy for linear
stable dynamic systems. The design of controller relax the minimum phase
assumption. The controller design procedure in this strategy is same for the
minimum and non-minimum phase systems. The order of the plant is not
needed for the controller design procedure.
2. Development of the adaptive fuzzy internal model control of thermal
heating process
The stability and convergence proofs of the closed-loops are established using the
theory of linear adaptive control systems. The results are verified using the computer
simulation examples and laboratory scale experimental setups dc-motor, flow and
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level control of liquids and temperature control of a time delay thermal process.
These techniques several other authors used for the control of different plants.
C. Direct Adaptive Inverse Control (DAIC) [14]
This method is a modification of the indirect adaptive inverse control (IAIC). DAIC is
computationally efficient technique to accomplish the adaptive tracking of linear
stable plants. The stability of the closed-loop and convergence of the tracking error
to zero has been proved.
D. U-model Based Control[15-21]
U- model is a control oriented model of the nonlinear dynamic system. This model
simplifies the controller design procedure. These controllers are computationally
efficient. Our main contributions in this area are
1. Internal Model Control for Nonlinear Plants using U-model Based
2. Adaptive Tracking of Nonlinear Plants Using U-model
3. MIMO U-model based control: real-time tracking control and feedback
analysis via small gain theorem
4. Utilizing Higher-Order Neural Networks In U-Model Based Controllers For
Stable Nonlinear Plants
5. U-model Based 2-DoF Multi-variable IMC for Improved Input-Disturbance
Rejection: A Case Study on a 2-Link Robot Manipulator
The stability and error convergence of the closed-loops in all of the above
developments has analyzed using the nonlinear dynamic systems theory. All the
proposed techniques can be used for the adaptive tracking of stable nonlinear
plants. The theoretical results have been verified using computer simulation and
laboratory scale experimental beds. Path tracking of the robotic manipulators is also
accomplished using these techniques. The review of the above techniques has
been published in [22].
E. Lyapunov Function Based Neural Network[23-25]
Artificial neural networks have been extensively used as adaptive inverse
controllers. However, the majority of available neuro-adaptive inverse controllers
are associated with two significant problems. First, the neural networks are trained
with the conventional gradient descent backpropagation learning algorithms that
suffer from slow convergence and frequently trap at the local minima of the error
cost function. Second, these adaptive control techniques do not establish
mathematical foundations for error convergence and closed-loop stability
conditions. Instead, they assume the convergence and stability based on the
Certainty Equivalence Principle, which is not a realistic assumption. These problems
make the performance of the adaptive controller unreliable.
A neuro-adaptive inverse control technique for single-input single-output dynamic
plants that overcomes the aforementioned problems is proposed and discussed in
this research work. A Lyapunov function based backpropagation learning algorithm
for neural network training has been presented. The proposed backpropagation
algorithm guarantees fast convergence and assures single global minimum with
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adaptive adjustment of the network parameters. Moreover, an adaptive inverse
control architecture has been presented that uses two Lyapunov function neural
networks in a unified framework. In this scheme, one neural network acts as the
inverse dynamics controller whereas the other functions as an estimator to calculate
the control command. The error convergence and closed-loop stability of the
inverse controller have been proved with the Lyapunov Stability Theory.
Furthermore, the controller performance has been studied with four simulation
examples and two laboratory-scale experimental setups. These case studies show
that tracking of the continuous trajectories is achieved and local minima trapping
is not observed. The simulation and experimental results validate theoretical findings.
F. Interdisciplinary Research [26-27]
There are two contribution in the control of power plants.
1. Transient, permanent faults of power lines, and the consequent switching of
the associated circuit breakers are represented as a discrete-time Markov
chain. The controller is designed for Markov jump linear systems based on
transition probabilities obtained from statistical data of the faults. The linear
matrix inequalities framework is used as a tool for designing the proposed
controller. The controller provides desired performance swiftness via regional
pole placement with the constraint of system load variations and random
variations in the topology. The effectiveness of the power system stabilizer is
studied on a single-machine infinite-bus and multi-machine systems.
2. This study proposes a novel approach for the design of an indirect adaptive
fuzzy output tracking excitation control of power system generators proposed.
The method is developed based on the concept of differentially flat systems
through which the nonlinear system can be written in canonical form. The
flatness-based adaptive fuzzy control methodology is used to design the
excitation control signal of a single machine power system in order to track a
reference trajectory for the generator angle. The considered power system
can be written in the canonical form and the resulting excitation control signal
is shown to be nonlinear. In case of unknown power system parameters due
to abnormalities, the nonlinear functions appearing in the control signal are
approximated using adaptive fuzzy systems. Simulation results show that the
proposed controller can enhance the transient stability of the power system
under a three-phase to ground fault occurring near the generator terminals.
I consider these two research contributions as interdisciplinary because the
problems associated with the power generation system are solved using the control
theory.
G. Transdisciplinary Research [28-31]
The main contributions are given as
1. This study provides an extension of pathloss analysis in Urban environments in
Oman. Artificial Neural Network (ANN) are used to forecast the data for a
large distance. These trained neural nets are used to make desired forecasts.
These results are acceptable and can be used for OMAN.
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2. In this study, we propose a new approach for online detection of pileup in
gamma-ray spectroscopy using finite length deconvolution filters. The
approach was tested in resolving pileup with an average success of 93%. We
show that the number of pileup events can be reduced by eightfold using the
proposed approach. Gamma pulses, from a 3 inch Na(Tl) scintillation
detector, were captured as single and double pulses for the purpose of
testing the proposed peak detection algorithm. The algorithms developed
here were then implemented in real time using a high performance floating-
point processor, TMS320C671.
3. Traditionally, sensors in wireless sensor networks are designed to collect data
from the area of interest and forward it to the base-station. In periodic sensing,
a prior knowledge about the data collected by the sensor helps in making
the sensor more sophisticated. In this study, a spline curve fitting model is built
using past data of the sensor. This computational model is embedded with
the sensor and at the user-node. The model helps to predict the current
observed value knowing the past readings of the sensor. If relative-error
between the calculated and the observed value by sensor is less than certain
threshold, the sensor could schedule itself to stay idle instead of being in
transmission mode. The same model installed on the user-node could be used
to obtain the approximated observed value. The proposed scheme uses a
decentralized scheduling algorithm which is generic and easy to implement.
4. The nodes in wireless sensor networks are prone to failure due to fading
energy. The wireless sensor network applications that require continuous data-
supply, suffer due to energy limitation of the sensor nodes. A technique based
on soft- sensing principles is employed in this study to assure the availability of
data to the applications, where there is no compromise on data acquisition
due to node failure. The computer simulations suggest that the proposed
methodology can be used effectively in where reliability is a high priority.
The above research contributions have proposed solutions to the problems
appearing in the various disciplines of science and engineering using the theory of
signal processing and identification.
H. Ongoing Research work
Presently, I am working with other team members on the following problems,
1. Development of the time efficient neural networks for the adaptive tracking
of noninvertible nonlinear dynamic systems
2. Development of the synchronization of chaotic systems with different order
3. Time efficient tracking of the plants with input constraints
4. Development of control algorithms for the autonomous mobile robots
5. Development of the control algorithms for the smooth operation of smart and
micro grids
6. Green energy storage systems
7. Developing automation system for the nano-cell based water treatment
plants
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I. Research Funds
Summary of the research funds obtained for different projects is given in the table.
NO Title Funding Sources Year Amount in
USD
1 Adaptive Tracking Based on FIR Filters SABIC 2003 32000/00
2 NL Systems Adaptive Tracking Fast Track KFUPM 2005 32000/00
3 Prototype Development of a Commercial-
Scale Retrofitting CNC System
CCSE Innovative
Research 2005 13350/00
4 Adaptive Control of Robotic Arm HEC Pakistan 2007 13000/00
5 Nonlinear PD Position Tracking controller for
Robot Manipulators
SQU Internal Grant
for Research 2010 13000/00
6 VTOL Three-Dimensional vector motion
control and tracking FURAP/TRC Oman 2014 6230/00
7 Smart Dolphins Protection System FURAP/TRC Oman 2015 6230/00
8 Omani Wheel Robot Control IG/ENG/ECED/17/02 2017 15000/00
Total is U SD 130810/00
[1]. Jianming Lu, Muhammad Shafiq and Takashi Yahagi, “A Method for Adaptive Control of
Nonminimum Phase Continuous-Time Systems Based on pole-zero Placement”, Trans. IEICE, vol.
E80-A, no. 6, June 1997. [2]. Jamming Lu, Muhammad Shafiq and Takashi Yahagi, “Robust Model Matching Control for
Linear Discrete-Time Systems”, Journal of Signal Processing, vol. 1, no. 2, pp. 117-124, March
1997. (in Japanese) [3]. Jianming Lu, Muhammad Shafiq and Takashi Yahagi, “A Design Method of Model Reference
Adaptive Control for SISO Nonminimum Phase Continuous-Time Systems using Approximate
Inverse System”, Trans. IEE of Japan, vol. 117–C, no. 3, pp. 315-321, March 1997. (in Japanese) [4]. Jianming Lu, Muhammad Shafiq and Takashi Yahagi, “A New Method for Self-Tuning Control of
Nonminimum Phase Discrete-Time System with Disturbances”, Trans. IEE of Japan, vol. 117–C,
no. 2, pp 110-116, February 1997.
[5]. Muhammad Shafiq, Jianming Lu and Takashi Yahagi, “On Self–Tuning Control of Nonminimum
Phase Discrete- time Stochastic System”, Trans. IEICE, vol. E79-A, no. 12, pp. 2176-2184,
December 1996. [6]. Jianming Lu, Muhammad Shafiq and Takashi Yahagi, “Model Reference Adaptive Control for
MIMO Nonminimum phase Discrete-Time Systems Using Approximate Inverse Systems”, Trans. IEE
of Japan, vol. 116-C, no 5, pp. 750-576, May 1996.
[7]. Muhammad Shafiq, Jianming Lu and Takashi Yahagi, “A New Method for Self Tuning Control of
Nonminimum Phase Continuous-Time Systems Based on Pole-Zero Placement”, Trans. IEICE, vol.
E79A, no. 4, pp. 578-584, April 1996. [8]. Jianming Lu, Muhammad Shafiq and Takashi Yahagi: Vibration Control of Flexible Robotic Arms
by Using Robust Model Matching Control, Proc. of the 4th International Workshop on advanced
Motion Control, Mie, Japan, pp. 663-668, March 1996.
[9]. Jianming Lu, Muhammad Shafiq and Takashi Yahagi: Model Reference Adaptive Control for
Nonminimum Phase Systems and Its Application to DC Servo Motor System, Proc. of the 4th
International Workshop on Advanced Motion Control, Mie, Japan, pp. 208-212, March 1996. [10]. Muhammad Shafiq, Jianming Lu and Takashi Yahagi: On Self-Tuning Control of Nonminimum
Phase Discrete-Time Stochastic System, Proc. Of the 22nd International Conference on Industrial
Electronics, Control and Instrumentation, Taipei, Taiwan, pp. 340-345, August 1996.
[11]. Muhammad Shafiq, “FIR Filters based Adaptive Tracking”, Trans. IEICE, vol. E87-A, no. 3,
pp.716-724, March 2003. [12]. Muhammad Shafiq and Mohammad Haseebudin, “U-model Based Internal Model Control for
Page 40 of 40
Nonlinear Plants”, IMechE, Part I, vol. 16, no. 10, pp. 449-458, Oct. 2005.
[13]. Muhammad Shafiq and Agus R Widodo, “Adaptive fuzzy internal model control of thermal
heating process”, IEICE Electronics express, vol. 1, no. 6 , June 25, 2004. [14]. Muhammad Shafiq and Muhammad A. Shafiq, “Direct Adaptive Inverse Control”, IEICE
Electronics express, vol. 6, no. 5, pp. 223-229, March 2009.
[15]. S. Saad Azhar Ali, Fouad M. Al-Sunni and Muhammad Shafiq, “U-model Based 2-DoF Multi-
variable IMC for Improved Input-Disturbance Rejection: A Case Study on a 2-Link Robot
Manipulator”, Int J Adv Robotic Sy, vol. 8, no. 4, pp. 166-175, August 2011. [16]. Muhammed Shafiq and Naveed R. Butt, “Utilizing higher-order neural networks in U-model
based controllers for stable nonlinear plants”, International Journal of Control, Automation and
Systems, Springer, vol. 9, no. 3, 489-496, June 2011. [17]. S. Saad Azhar Ali, Fouad M. Al-Sunni, Muhammad Shafiq and Jamil M. Bakhashwain, “U-model
based learning feedforward control of MIMO nonlinear systems”, Journal of Electrical
engineering, Springer, vol. 91, no. 8, pp. 405-415, April 2010. [18]. Syed Saad Azhar Ali, Muhammad Shafiq, Fouad M. Al-Sunni and Jamil M. Bakhashwain, “MIMO U-
model based control: real-time tracking control and feedback analysis via small gain theorem”,
WSEAS Transactions on Circuits and Systems, vol. 7, no.7, pp. 610-619, July 2008. [19]. Muhammad Shafiq and Naveed R. Butt, “Real-time adaptive tracking of DC motor speed using U-
model based IMC”, Automatic Control and Computer Sciences, vol. 41, no. 1, pp. 45-54, Jan.
2007. [20]. Naveed Butt and Muhammad Shafiq, “On the adaptive Tracking of Nonlinear Plants Using U-
model”, IMechE, Part I, vol. 220, no. 6, pp. 473-387, Dec. 2006. [21]. Muhammad Shafiq and Mohammad Haseebudin, “U-model Based Internal Model Control for
Nonlinear Plants”, IMechE, Part I, vol. 16, no. 10, pp. 449-458, Oct. 2005.
[22]. Quanmin Zhu, Yongji Wang, Dongya Zhao, Shaoyuan Li and Stephen A. Billings, “Review of
rational (total) nonlinear dynamic system modelling, identification, and control”, International
Journal of Systems Science, Vol. 46, No. 12, 2122–2133, 2013. [23]. Muhammad Saleheen Aftab and Muhammad Shafiq, “Neural networks for tracking of unknown
SISO discrete-time nonlinear dynamic systems”, ISA Transactions, vol. 59, no. 11, pp. 363-374,
2015.
[24]. Muhammad Saleheen Aftab, Muhammad Shafiq and Hassan Yousef: Lyapunov stability
criterion based neural inverse tracking for unknown dynamic plants, 2015 IEEE International
Conference on Industrial Technology ICIT, Seville, Spain, March 17-19, 2015. [25]. Muhammad Saleheen Aftab and Muhammad Shafiq: Adaptive PID controller based on
Lyapunov function neural network for time delay temperature control, IEEE 8th GCC Conference
and Exhibition (GCCCE), Muscat, Oman, Feb. 1-4, 2015, 2015.
[26]. Hisham Soliman and Muhammad Shafiq, “Robust Stabilisation of Power Systems with Random
Abrupt Changes”, IET Generation, Transmission & Distribution, 8 pages, 2015.
[27]. Hassan A. Yousef, Mohamed Hamdy, Muhammad Shafiq, “Flatness-based adaptive fuzzy
output tracking excitation control for power system generators”, Journal of the Franklin Institute,
vol 350, no. 8, pp. 2334-2353, October 2013. [28]. Zia Nadir, Muhammad Bait-Suwailam and Muhammad Shafiq, “RF Coverage Analysis and
Validation of Cellular Mobile Data using Neural Network”, International Journal of Neural
Networks and Advanced Applications, vol. 1, pp. 30-36, 2014
[29]. Muhammad WRaad , Mohamed Deriche, James Noras and Muhammad Shafiq, “A novel
approach for pileup detection in gamma-ray spectroscopy using de-convolution”, Meas. Sci.
Technol, vol. 19, no. 5, pp. 1-6, May 2008. [30]. Rubina Sultan, Noor M. Khan and Muhammad Shafiq: Low Duty-Cycling with Spline-based
Curve fitting of Sensor Data in Wireless Sensor Networks, IEEE NCM’08, Fourth International
Conference on Networked Computing and Advanced Information Management, Gyeongju,
South Korea, September 02-04, 2008. [31]. Rubina Sultan, Muhammad Shafiq, and Noor M. Khan: Reliability in Wireless Sensor Networks
Using Soft Sensing, IEEE 7th Computer Information Systems and Industrial Management
Applications, Ostrava, The Czech Republic, June 26 - June 28, 2008.