Keynote Speakers
C. L. Philip Chen received his M.S. degree from
the University of Michigan, Ann Arbor, Michigan, U.S.A. in 1985, and
his Ph.D. degree from Purdue University, West Lafayette, Indiana,
U.S.A., in 1988, both degrees in Electrical Engineering. He was with
Wright State University, Department of Computer Science and
Engineering, from 1989 to 2002 as an assistant, an associate, and a
full professor before he joined the University of Texas, San
Antonio, where he has been a Professor and Chair of the Department
of Electrical and Computer Engineering, the Associate Dean for
Research and Graduate Studies of the College of Engineering.
Currently, he is Chair Professor and the Dean of Faculty of Science
and Technology, University of Macau.
Dr. Chen has been a visiting research scientist at the Materials
Directorate, U.S. Air Force Wright Lab. He has been a senior
research fellow sponsored by the U.S. National Research Council and
a research faculty fellow for NASA Glenn Research Center for several
years. His current research interests include theoretical
development in computational intelligence, intelligent systems,
robotics and manufacturing automation, networking, diagnosis and
prognosis, life prediction and life-extending control. Credited to
his technical contribution, he is an elected IEEE Fellow and AAAS
Fellow (www.aaas.org).
Dr. Chen has been active in many IEEE international conference
services and publications as a Program Chair and Organizing
Committee. He is the General Chair of the 2009 IEEE Systems, Man,
and Cybernetics (SMC) annual conference, the General Co-Chair of
2008 IEEE SSIRI (Secure System Integration and Reliability
Improvement), a Program Co-Chair of 2008 & 2010 ICMLC. He is the
founding SMC Chapter Chair at Central Texas Section, Macau Chapter,
and founding Co-Chairs of three SMCS Technical Committees (SoS,
Enterprise Information Systems, and Information Assurance and
Intelligent Multimedia). Currently, he is the Vice President on
Conferences and Meetings of IEEE SMC Society, where he has been the
VP on Technical Activities in Systems Science and Engineering, a
member of IEEE SMC Board of Governors and Treasurer and serves as an
Associate Editor of IEEE Transactions on SMC-C and IEEE Systems
Journal. As a result of his assiduous service, he received
Outstanding Contribution Award in 2008. In addition, he is a member
of Tau Beta Pi and Eta Kappa Nu honor societies. On education and
academic service, Dr. Chen is the founding faculty advisor of IEEE
Computer society student chapter and has been the faculty advisor of
the Tau Beta Pi engineering honor society at the University of Texas
at San Antonio. In addition, he is a certified ABET (Accreditation
Board of Engineering and Technology Education) Computer Engineering,
Electrical Engineering, and Software Engineering program evaluator.
Life Extending and Adaptive Sensor
Fault Detection and Identification in Health Monitoring Systems
We have modeled and calculated the probability of failure due to
component damage. Using this model, a Monte Carlo simulation is also
performed to evaluate the likelihood of damage accumulation under
various operating conditions. Using thermal mechanical fatigue (TMF)
of a critical component as an example, it has been shown that that
an intelligent acceleration algorithm can drastically reduce life
usage with minimum sacrifice in performance. By means of genetic
search algorithms, optimal acceleration schedules can be obtained
with multiple constraints. The simulation results show that an
optimized acceleration schedule can provide a significant life
saving in selected engine components.
Usually, solutions to sensor validation fall into two major
categories: the data-based approaches and the model-based
approaches. Model-based methods include nonparametric and parametric
approaches. Belonging to the first category are neural-network-bank
based approaches. The non-parametric methods are more robust, but a
large number of training data are needed nevertheless. On the other
hand, parametric approaches, including dynamic state space models
(DSSM), provide better accuracy and tracking performance without the
need of training. The price paid here is the need for high fidelity
real-time system models. Particle filter (PF) is an alternative name
for sequential importance sampling for DSSM. PF has been commonly
employed to online processing of dynamic systems described by DSSM.
We will also discuss a Markov jump DSSM (MJDSSM) for system modeling
and mixture Kalman filter (MKF) solution-- a unique and efficient
particle filtering detector being developed.
The ultimate goal of engine health monitoring is to maximize the
amount of meaningful information to perform diagnostics and
prognostics on engine health. To achieve highest level of
intelligence in different levels and aspects, in the future work, we
propose to implement the concept of data fusion that integrates data
from multiple sources to obtain improved accuracy and more specific
results.
Note: The presented work is funded by NASA and U.S. Air Force of
Scientific Research.
Madan M. Gupta
Dr. Madan M. Gupta is a professor (Emeritus)
and holds the position of Distinguished Research Chair in the
College of Engineering and is the director of the Intelligent
Systems Research Laboratory at the University of Saskatchewan.
Dr. Gupta's current research interests are in the areas of neuro-vision
systems, neuro-control systems, integration of fuzzy-neural systems,
neuronal morphology of biological vision systems, intelligent and
cognitive robotic systems, cognitive information, new paradigms in
information processing, chaos in neural systems, and fuzzy-neural
logic in law. He is also developing some new architectures of
computational neural networks and computational fuzzy neural
networks for application to advanced robotics, aerospace, medical,
industrial, and business systems and law. His interest also lies in
signal and image processing with applications to medical systems.
Dr. Gupta has authored or co-authored over 825 published research
papers. He has recently co-authored the seminal book Static and
Dynamic Neural Networks: From Fundamentals to Advanced Theory. Dr.
Gupta has previously co-authored Introduction to Fuzzy Arithmetic:
Theory and Applications (the first book on fuzzy arithmetic) and
Fuzzy Mathematical Models in Engineering and Management Science.
Both of these books have Japanese translations. Also, Dr. Gupta has
edited or co-edited 19 other books as well as many conference
proceedings and journals in the fields of his research interests
such as adaptive control systems, fuzzy computing, neuro-computing,
neuro-vision systems, and neuro-control systems.
Dr. Gupta received his B.E. (Hons.) and M.E. degrees in
electronics-communications engineering from the Birla Engineering
College (now the Birla Institute of Technology & Science), Pilani,
India, in 1961 and 1962, respectively. As a commonwealth scholar, he
received his Ph.D. degree from the University of Warwick, United
Kingdom, in 1967 in adaptive control systems. In the fall of 1998,
for his extensive contributions in neuro-control, neuro-vision, and
fuzzy-neural systems, Dr. Gupta was awarded an earned doctor of
science (D.Sc.) degree by the University of Saskatchewan.
Dr. Gupta was elected fellow of the Institute of Electrical and
Electronics Engineers (IEEE) for his contributions to the theory of
fuzzy sets and adaptive control systems and for the advancement on
the diagnosis of cardiovascular disease. He was elected fellow of
the International Society for Optical Engineering (SPIE) for his
contributions to the field of neuro-control and neuro-fuzzy systems.
He was also elected fellow of the International Fuzzy Systems
Association (IFSA) for his contributions to fuzzy-neural computing
systems.
In 1998, Dr. Gupta was honored by the III- Kaufmann Prize and Gold
Medal for his research in the field of fuzzy logic. This Gold Medal
was presented by the Foundation FEGI (Fundacio per a l'Estudi de la
Gestio en la Incertesa: Fuzzy Management Research Foundation) and
SIGEF (Sociedad Internacional de Gestion Economia: Fuzzy,
International Association for Fuzzy Set Management and Economy) in
Reus, Spain. In 1991, Dr. Gupta was the co-recipient of the
Institute of Electrical Engineering Kelvin Premium. He was elected
as a visiting professor and a special advisor in the area of high
technology to the European Centre for Peace and Development (ECPD),
University for Peace, which was established by the United Nations.
In 1991, he was invited by the ECPD to visit and lecture at about
five industrial and research centers in India.
Dr. Gupta is or has been on the editorial board of over fifteen
journals in the field of fuzzy- neural and intelligent systems.
Also, he has participated in the initiation of some of these
journals. He has also served as a founding member of some of the
international societies such as International Fuzzy Systems
Association (IFSA), North American Fuzzy Information Processing
Society (NAFIPS) and Canadian Fuzzy Information and Neural Society
(CAN-FINS).
On the Design of Error-Based Adaptive Controller: Some Performance and Stability Considerations
Abstract:
Design of an adaptive controller for complex dynamic systems is a
big challenge that researchers are facing The Performance and
stability in control systems are extremely important considerations
in engineering systems. It is broadly known that in linear
time-invariant systems, the stability of the system is guaranteed
with a proper design of a linear controller. However, the
performances of the system responses are not the same by using
different linear controllers. The system may respond with or without
oscillations. The transient response with oscillations becomes very
fast, although some high amplitude of the oscillation may affect the
stability of the system. The transient response without oscillations
becomes slower, but the system is very stable. From an engineer’s
point of view, both the oscillations of the system and the slow
system response are not acceptable. It is desired to have system
response fast, stable, and with no oscillations, but this is not
achievable with a linear controller.
In this paper we introduce the
design of an error-based adaptive controller (E-BAC) using the new
notion of dynamic pole motion (DPM) for a general class of dynamic
system, linear, nonlinear, or timevarying. The purpose of this novel
approach is to design an error-based 2
adaptive controller (E-BAC) that makes the system response
reasonably fast with no overshoot and with guaranteed stability. The
E-BAC has two dominant dynamic parameters, the dynamic position
feedback and the dynamic velocity feedback. In the design of the
novel nonlinear controller, the parameters of position feedback,
Kp(e,t), and velocity feedback,
Kv(e,t), of the
controller are designed as functions of the system error e(t). In
these feedback parameters, the position feedback controls the system
bandwidth, thereby the rise time in step response, whereas the
velocity feedback controls the damping ratio of the system
thereby the overshoot in the step response. In this design, for
large errors, we make the position feedback large, and thus we
increase the bandwidth of the system thereby yielding a smaller rise
time. Whereas, for decreasing errors, the position feedback is
continuously decreased to a small value which decreases the
bandwidth of the system. Similarly, but contrarily, the damping
ratio which is controlled by the velocity feedback, is kept very
small for large errors, and it is continuously increased to a large
value for decreasing error. Hence, in the design of the proposed
error-based adaptive controller, the position feedback Kp(e,t) and the velocity feedback
Kv(e,t) are formulated as
functions of the system error. This novel approach for formulating
the adaptive controller yields a very fast response with no
overshoot, and the design methodology presented here completely
assures the stability of the controlled system.
