Department of Systems, Control,
and Industrial Engineering
606 Olin Building (7070)
phone 368-4033; fax 368-3123
Howard Chizeck
e-mail: hjc2@po.cwru.edu
Systems and control engineering involves the design and problem solving in an
integrated, 'big picture' way that is technologically adaptable and is not tied to any single
type of problem solution. Systems and control engineers analyze and solve engineering
problems that combine different technologies and that cross the boundaries between
disciplines. The department has played a major and pioneering role in the development of
systems engineering as an academic, research, and professional discipline. The origins of the
department's programs go back to 1953. We conduct theoretical research in control and
mathematical systems theory and in decision analysis. Our goal is to have a healthy mix of
theory and application: the development of theory brings insights that lead to new
applications; its application to real-world problems motivates further development of
theory.
The faculty of the department are all actively engaged in undergraduate and graduate
teaching and in research. We believe that research and education are best done in concert.
Since many systems can contain many different types of component parts (e.g., electrical
components, mechanical components, chemical components, biological components,
economic components), we approach system design and analysis in a way that is not
restricted to a particular sub-specialty. Thus our graduates are especially capable of adapting
to technological change.
The department offers a B.S.E. degree in systems and control engineering (the first of
its kind to be accredited by the Accreditation Board for Engineering and Technology), an
M.S. in systems engineering and control engineering, and the Ph.D. degree.
Howard Chizeck, Sc.D. (Massachusetts Institute of Technology)
Professor and Chair
Adaptive control and system identification, stochastic and nonlinear control theory;
biomedical applications control engineering; control of neural prostheses intelligent control
systems.
Marcus R. Buchner, Ph.D. (Michigan State University)
Associate Professor
Computer simulation of complex systems; control of industrial systems; analysis of
discrete event and hybrid systems.
Vira Chankong, Ph.D. (Case Western Reserve University)
Associate Professor
Optimization theory and methods; multiobjective optimization; decision theory; risk
analysis, large scale system analysis, production system planning and
analysis.
Wei Lin, Ph. D. (Washington University)
Assistant Professor
Nonlinear systems control and stability theory; system identification and adaptive
control; applications of nonlinear control.
Kenneth A. Loparo, Ph.D. (Case Western Reserve University)
Professor and Associate Dean of Engineering
Stability and control of nonlinear and stochastic systems; analysis and control of
discrete event systems; intelligent control systems, system failure diagnosis and
prediction.
Behnam Malakooti, Ph.D. (Purdue University)
Professor
Industrial engineering; detection of tool wear, computer-aided manufacturing;
multiple-criteria decision making and optimization.
Mihajilo D. Mesarovic, Ph.D. (Serbian Academy of Science)
Cady Staley Professor of Engineering
Large-scale systems theory; algebraic systems theory, multilevel systems analysis;
world and regional climate and resource modeling.
N. Sreenath, Ph.D. (University of Maryland)
Assistant Professor
Nonlinear systems: modeling, stability, and control; multibody systems; symbolic
computation; large scale systems; global climate change problems.
Coleman B. Brosilow, Ph.D. (Brooklyn Polytechnic Institute)
Professor of Chemical Engineering and Systems Engineering
Process control; inferential control; systems simulation.
Patricia Brennan, Ph.D. (University of Wisconsin, Madison)
Professor of Nursing and Systems Engineering
Applications of decision support methods, organizational modeling and optimization to
health care systems.
Joseph Koonce, Ph.D. (University of Wisconsin, Madison)
Professor of Biology and Systems Engineering
Systems control and application of systems concepts to biological and ecological
systems, aquatic ecology and systems ecology.
Stephen M. Phillips, Ph.D. (Stanford University)
Associate Professor of Electrical Engineering and Systems
Engineering
Nonlinear and adaptive control; sampled-data and multi-mode control; computer aided
control design and analysis.
Systems and control engineering is a 'cross-disciplinary' discipline. Most engineers
seek problem solutions from their own discipline. Electrical engineers tend to design
electrical solutions, and mechanical engineers design mechanical solutions to engineering
problems. Systems and control engineers analyze and solve engineering problems that
combine different technologies, and that cross the boundaries between disciplines. Many of
the fundamental tools of the field arise from applied mathematics. Our field is related to
computer engineering, since computers are extensively used in the design and analysis
process, and as component parts for many of the systems that we build. It is also related to
computer science, since we rely upon algorithmic and mathematical descriptions of system
structure and operation. The range of system design, analysis and control problems includes
industrial and management systems, biological and ecological systems and economic
systems.
The systems and control engineering B.S. program provides the student with the basic
concepts, analytical tools, and engineering methods which are useful in analyzing and
designing complex technological systems. and non-technological systems. Problems relating
to modeling, decision making, control, and optimization are studied. Some examples of
systems problems which are studied include computer control of industrial plants,
development of decision models for control of pollution, control of physiological processes,
and optimal planning and management of energy resources in large-scale systems. In each
case, the relationship and interaction among the various components of a given system
must be modeled. This information is then used to determine the best way of coordinating
and regulating their individual contributions, so as to achieve the overall goal of the system.
What may be best for an individual component of the system may not be the best for the
system as a whole.
There are three elective sequences available within our B.S. degree curriculum: Control
Systems, Systems Analysis and Industrial and Manufacturing Systems. The Control Systems
sequence is directed toward developing skills in dynamic system modeling, analysis,
automation and remote control, real-time data acquisition and feedback control. The
Systems Analysis sequence focuses on optimization, decision making and planning methods.
The Industrial and Manufacturing Systems sequence provides education in the application of
systems analysis, decision making and automation methods to industrial production and
manufacturing problems. All three sequences use concepts of modeling, data analysis,
computer simulation, and optimization. Computers play a central role in the systems and
control curriculum, not only for engineering and mathematical computation, but also for
computer simulation, automatic control, real-time data acquisition and signal
processing.
Graduates of our B.S. program find positions in both private industry and in the public
(governmental) sector. About half enter graduate school. Our graduates are valued because
of the general purpose engineering problem solving skills that they possess, and because
they are especially capable of adapting to technological change. A five-year Bachelor of
Science (engineering or mathematics)/Master of Science (systems and control engineering)
program is available for qualified students. A minor in Systems and Control Engineering is
also available.
A total of five courses (15 credit hours) are required to obtain a minor in Systems and
Control Engineering. At least nine credit hours must be selected from the following list: ESCI
212, ESCI 214, ESCI 304, ESCI 352, ESCI 346. The remaining credit hours can be chosen
from available courses in the Department of Systems, Control and Industrial Engineering and
require written approval by the faculty member in charge of the minor program. A suggested
list of courses is:
ESCI 110, Problem Solving & Systems Engineering ESCI 322, Simulation Methods
in Engineering ESCI 313, Signal Processing
ESCI 306, Control Engineering II
ESCI 250, Production Systems Engineering ESCI 350, Manufacturing Systems
Engineering
Six (6) of the fifteen (15) credits required for the minor program can be used to satisfy
the B.S. requirements for the major degree program, so a total of nine (9) credit hours of
additional course work are required for the minor program.
Graduate programs in Systems and Control engineering include the following areas of
concentration: control theory (adaptive control, stochastic filtering and control, nonlinear
control), optimization and decision theory (multi-objective and large scale system theory),
control of industrial and manufacturing systems (facilities layout, flexible manufacturing),
biomedical control system design and analysis (control of neural prostheses, automatic
control of therapeutic drug delivery), energy systems (power distribution and production
planning, load forecasting), and global and environmental system analysis and control.
Research funds are used to provide assistantships that support the thesis research of
graduate students. Current research funding is provided by Elsag-Bailey, Rockwell
Automation, the Ford Motor Company, the Cleveland Advanced Manufacturing Program
(CAMP), the Electric Power Research Institute (EPRI), the National Institutes of Health (NIH),
the National Science Foundation (NSF), the U.S. Department of Veterans
Affairs-Rehabilitation Research and Development Program (VA-RR&D), the Office of
Naval Research (ONR), the U.S. Agency for International Development (US-AID) and
UNESCO.
Thesis research may be directed toward the development of theory or methods for use
in systems analysis, design, control, or management. Areas of current research include the
following:
BIO-CONTROL ENGINEERING: the application of control theory and methods to
problems of biomedicine, including situations where the control system is regulating
physiological variables of a patient, as well as situations where the control system pertains
to an on-board assistive device. Research topics include: (1) Control of Functional Electrical
Stimulation: the development, control and systems engineering of on-board computer-based
assistive devices that provide paraplegic individuals with the ability to stand, walk, climb
stairs, and perform other maneuvers. Work done in the department provides systems
engineering and control support to a larger interdisciplinary effort in Cleveland, which
currently involves approximately $5 million per year of federal, state and local government
funding; (2) Closed Loop Drug Delivery: the design and analysis of computerized systems
that provide automatic regulation of the delivery of therapeutic drugs, for patients in surgery
or an intensive care units. The goal is to improve medical care, through the more exact
delivery of prescribed dosages.
CONTROL APPLICATIONS: Topics include: (1) The development of anti-lock braking
systems using fuzzy logic control methods; (2) Development of methods of automotive
control and computer assisted tools for engineering analysis and design (e.g., development
of computer based tools for system level failure mode effect analysis); (3) Developing
technology for advanced power train, energy management, sensing and control strategies
for electric vehicles; (4) the use of methods of control engineering to solve problems
involving industrial and manufacturing processes.
CONTROL AND FILTERING THEORY: Topics include: (1) nonlinear control theory work
addressing questions regarding the behavior, stability and control of dynamic systems that
are inherently nonlinear in the relationships between their inputs, outputs, and internal
states; (2) stochastic control theory work involving the study of the behavior, stability and
control of dynamic systems that possess a element of randomness in their operation over
time; (3) stochastic filtering theory work, investigating the extraction of information about
internal variables of a system on the basis of (possibly noise corrupted) measurements of
system outputs.
FAULT DETECTION AND DIAGNOSIS: research combining advanced theoretical topics
with solutions to industrial problems of high relevance and economic importance. Topics
include: (1) the detection specific identification of failure events in systems and, when
possible, the detection of incipient failures, through the use of nonlinear filtering of
measured system inputs and outputs; (2) the use of nonlinear dynamics and chaos theory
for failure detection: the use of chaos concepts and other advanced model-based methods
for vibration signature analysis
IDENTIFICATION AND ADAPTIVE CONTROL: research directed towards specific
application problems and the development of new theory. Topics include: (1) adaptive
control of nonlinear systems, adaptive control of multi-input, multi-output systems having
unknown and time varying input-output delays; (2) predictive adaptive control of
non-minimum phase systems and the development computationally efficient methods of
predictive control; (3) development and application of methods for real-time identification of
parameters for linear systems having unknown input-output delays, and for nonlinear
systems.
INTELLIGENT SYSTEMS-NEURAL NETS AND FUZZY LOGIC: the use of methods of
Ômachine intelligence' to accomplish control of systems. Particular topics of interest
include: (1) the use of feedforward artificial neural nets to detect tool wear in parts
machining processes, and to model load demand of electric power systems; (2) the use of
fuzzy logic methods to attain anti-lock braking for automobiles, to control manufacturing
processes and chemical processes, to detect events of gait in neuro-prosthetic systems that
provide walking for paraplegics using electrical stimulation; (3) the analysis of combined
discrete and continuous state Ôhybrid' dynamical systems.
MATHEMATICAL MODELING AND SYSTEMS ANALYSIS OF GLOBAL CHANGE
PHENOMENA: the use of mathematical modeling of global economic and physical
phenomena, in conjunction with computer simulation, to develop alternative scenarios of the
future. This work involves a determination of what changes are possible within an
environmental system, on the basis of the structure of mathematical models that represent
its behavior (or hypotheses about its behavior).
OPTIMIZATION AND DECISION THEORY AND METHODS: basic theoretical work and
specific applications. Topics include: (1) Multi-objective optimization theory; (2) Algorithms
for machine part formation problems; (3) Clustering algorithms for data compression; (4)
Algorithms and tools for VLSI design; (5) Algorithms and methods for facility location and
layout in manufacturing systems; (6) the use of systems analysis and decision theory
methods to solve problems of the electric utility industry, such as quantification of the
implications of transmission constraints for generation costs and resource planning.; (7)
methods for the design of magnetic resonance imaging (MRI) pulse sequences, for clinical
MR imagers. to allow for the removal of motion artifacts (e.g., in images of the liver) and
enhancements of images specific tissue types; (8) the application of systems analysis and
decision theory methods to problems of information flow and control in health care.
Beginning in July 1996, the department will be housed in the sixth, seventh and eighth
floors of the Olin Building. An exciting aspect of this move is the establishment of new
laboratory facilities. The main teaching laboratories of the department include:
MICROCOMPUTER LABORATORY. This laboratory contains approximately 25
microcomputers (as of July 1996, these will be mostly high end Pentiums and a few
Macintosh Power PCs), along with a complement of laser printers, network connections
(university fiber optic network and LAN), and scientific software (MATLAB, VISSIM,
Mathematica, GINO, LINDO, etc.). COMPUTER WORKSTATION LABORATORY. This
laboratory contains various SPARC workstations, all operating under UNIX. All are
connected to the university fiber optic network.
PROCESS CONTROL LABORATORY. This laboratory contains process control pilot
plants, computerized hardware for process control and demonstration/research facilities.
This wetlab has access to steam and compressed air for use in the pilot plants.
DYNAMICS AND CONTROL LABORATORY. This laboratory contains mechanical,
pneumatic and electrical laboratory experiments for teaching and research purposes. This
includes PLCs, motors and robotic systems. As of the publication deadline for this Bulletin,
many specifics of the new facilities have not been finalized. The reader is encouraged to
contact the department (and check the department Web Site) for updated information about
laboratory facilities.
CWRUnet is a state-of -the-art high-speed fiber optic campus-wide local area computer
network which interconnects laboratories, faculty and student offices, classrooms, and
student dormitories at CWRU. CWRUnet is one of the largest fiber-to-desktop networks
anywhere. The data portion of the cabling is 100% fiber, so that with appropriate
optoelectronics and software, it is possible to attach devices, including high performance
workstations and associated print and file servers to CWRUnet. The network is currently
(May 1966) being upgraded to ATM running at 155 M bps (OC-3). Through this network,
users have access to a variety of on-campus and off-campus resources . It allows Internet
access for all machines on campus. Access to major programming languages and application
packages is available over the network. On-line databases such as EUCLID (the University
Libraries' circulation and public access catalog), and CD-ROM based dictionary, thesaurus
and encyclopedias are available. Many regional and national institutional library catalogs are
accessible over the network as well.
Systems and Control Engineering (ESCI)
ESCI 110, Problem Solving and Systems Engineering, 3
Art and science of engineering problem solving. Basic concepts of systems
engineering, control and optimization for effective problem solving. Use of computers for
model-building and analysis. Hands on experience in model building. Course includes
project.
ESCI 212, Signals, Systems and Control, 3
Characterization of continuous-time signals and systems. Laplace transforms,
Z-transforms, constant coefficient differential equations and difference equations. Fourier
methods. Introduction to control systems and design.
Prerequisite: MATH 224
ESCI 214, Signals, Systems and Control Lab, 1
A laboratory course based on the material in ESCI 212. Analysis and simulation using
matlab/simulink. Laboratory experiments involving signal processing and control.
ESCI 250, Production Systems in Engineering, 3
Time and motion study, human factors and safety engineering, man-machine systems,
quality control and reliability, project management, scheduling, sequencing, inspection and
maintenance of industrial processes. One of the following list of Statistics courses is a
corequisite: STAT 207, 243, 312, 313, 325, 332, or 345
ESCI 304, Control Engineering I with Laboratory, 3
Analysis and design techniques for control applications. Linearization of nonlinear
systems. Design specifications. Classical design methods: root locus, bode, nyquist. State
space modeling, solution, controllability, observability and stability. Modeling and control
demonstrations and experiments single-input/single-output and multivariable systems.
Control system analysis/design/implementation software.
Prerequisite: ESCI 212
ESCI 306, Control Engineering II with Lab, 3
Advanced techniques for control of dynamic systems. State-space modeling, analysis,
and controller synthesis; introduction to nonlinear control systems: phase plane methods,
bang-bang control, time-optimal control; describing functions analysis and design
techniques; discrete time systems and controllers.
Prerequisite: ESCI 304
ESCI 313, Signal Processing, 3
Fourier series and transforms. Analog and digital filters. Fast-Fourier transforms,
sampling, and modulation for discrete time signals and systems. Consideration of stochastic
signals and linear processing of stochastic signals using correlation functions and spectral
analysis.
Prerequisite: ESCI 212
ESCI 322, Simulation Techniques in Engineering, 3
Discrete event systems and simulation concepts. Discrete event simulation with Batch
and interactive languages.
Prerequisite is any one of the following: STAT 312, 313, 325, 332 or 345
ESCI 340, Introduction to Global Issues, 3
Methods for analysis and assessment of issues of world scope, and implications for
United States policies and interests. Technology, environment, economic growth, health and
value transformations. Report required.
ESCI 346, Engineering Optimization, 3
Optimization techniques including linear programming and extensions; transportation
and assignment problems; network flow optimization; quadratic, integer, and separable
programming; geometric programming; and dynamic programming. Nonlinear optimization
topics: optimality criteria, gradient and other practical unconstrained and constrained
methods. Computer applications using engineering and business case studies.
Prerequisite: MATH 201
ESCI 350, Manufacturing Systems Engineering, 3
Concepts, theories, and procedures for solving modern manufacturing, computerized
manufacturing processes, computerized discrete production systems, numerical control,
industrial robots, flexible manufacturing systems, group technology, materials handling
systems, man-machine requirements, and computerized facility layout design.
Prerequisite: ESCI 250
ESCI 351, Manufacturing Systems Laboratory, 1
Application of techniques developed in ESCI 350 using available software packages,
small scale robots and equipment, and real-time mini- and micro-computer facilities.
ESCI 352, Engineering Economics and Decision Analysis, 3
Economic analysis of engineering projects, focusing on financial decisions concerning
capital investments. Present worth, annual worth, internal rate of return, benefit/cost ratio.
Replacement and abandonment policies, effects of taxes, and inflation. Decision making
under risk and uncertainty. Decision trees. value of information.
ESCI 353, Accounting for Engineering, 1
Overview of corporate accounting practices and how they contribute to engineering
economic analyses. Cost accounting. Preparation and interpretation of financial
reports.
ESCI 355, Production Engineering Laboratory, 2
Development of experimental techniques for measuring costs, human activities, and
the physical characteristics of industrial systems. Hands-on experience with industrial and
manufacturing computer software packages to solve simulated and real-world problems.
Experimental design and error analysis. Field trips to selected local industries to provide
exposure to modern machines, tools, and manufacturing systems.
Prerequisite: ESCI 250, ESCI 350 and ESCI 351
ESCI 396, Special Topics, 1-36
Prerequisite is consent of supervising faculty member and department chair.
ESCI 398, Engineering Projects I, 3
Project experience in the application of course material to practical systems
engineering problems. Identification of project, literature review, and proposal preparation
for ESCI 399.
ESCI 399, Engineering Projects II, 3
Elective projects with emphasis on engineering design. Capstone engineering
project.
Prerequisite: ESCI 398
ESCI 401, Digital Signal Processing, 3
Characterization of discrete-time signals and systems. Fourier analysis: the
Discrete-time Fourier Transform, the Discrete-time Fourier series, the Discrete Fourier
Transform and the Fast Fourier Transform. Continuous-time signal sampling and signal
reconstruction. Digital filter design: infinite impulse response filters, finite impulse response
filters, filter realization and quantization effects. Random signals: discrete correlation
sequences and power density spectra, response of linear systems.
Prerequisite: ESCI 313
ESCI 404, Digital Control Systems, 3
Analysis and design techniques for computer based control systems. Sampling, hybrid
continuous-time/discrete-time system modeling; sampled data and state space
representations, controllability, observability and stability, transformation of analog
controllers, design of deadbeat and state feedback controllers; pole placement controllers
based on input/output models, introduction to model identification, optimal control and
adaptive control.
Prerequisite: ESCI 304
ESCI 408, Introduction to Linear Systems, 3
Analysis and design of linear feedback systems using state-space techniques. Review
of matrix theory, linearization, transition maps and variations of constants formula,
structural properties of state-space models, controllability and observability, realization
theory, pole assignment and stabilization, linear quadratic regulator problems, observers, and
the separation theorem.
Prerequisite: ESCI 304
ESCI 414, Complex Systems Modeling and Analysis, 3
Complexity and uncertainty are principal obstacles to deriving solutions in many
situations, in particular those situations which require multidisciplinary considerations and
are "poorly defined". This course deals with multilevel, hierarchical system theory applied to
complex systems including the use of an interactive software support system for dealing
with uncertainties. Global climate change will be used as the case study. Global models and
the software support system will be used in this course.
ESCI 416, Optimization Theory and Techniques, 3
Underlying theory of linear, nonlinear, multilevel, and multiobjective optimization.
Techniques include linear programming and extensions, quadratic programming, dynamic
programming, decomposition coordination schemes for multilevel optimization. Methods for
generating Pareto optimal solutions in multiobjective optimization. Applications to
engineering problems.
Prerequisite: MATH 201
ESCI 417, Introduction to Stochastic Control, 3
Analysis and design of controllers for discrete-time stochastic systems. Review of
probability theory and stochastic properties, input-output analysis of linear stochastic
systems, spectral factorization and Weiner filtering, minimum variance control, state-space
models of stochastic systems, optimal control and dynamic programming, statistical
estimation and filtering, the Kalman-Bucy theory, the linear quadratic Gaussian problem, and
the separation theorem.
Prerequisite: ESCI 408
ESCI 418, System Identification and Adaptive Control, 3
Parameter identification methods for linear discrete time systems: maximum likelihood
and least squares estimation techniques. Adaptive control for linear discrete time systems
including self-tuning regulators and model reference adaptive control. Consideration of both
theoretical and practical issues relating to the use of identification and adaptive
control.
ESCI 421, Optimization of Dynamic Systems, 3
Fundamentals of dynamic optimization with applications to control. Variational
treatment of control problems and the Maximum Principle. Structures of optimal systems;
regulators, terminal controllers, time-optimal controllers. Sufficient conditions for optimality.
Singular controls. Computational aspects. Selected applications.
Prerequisite: ESCI 408
ESCI 427, Risk and Reliability Methods for Engineers, 3
Probabilistic models and methods for risk, reliability, and quality engineering; Markov
decision processes; stochastic dynamic programming; stochastic programming and other
methods for risk analysis; failure models; qualitative fault analysis; reliability analysis of
systems; life data analysis and accelerated life testing; design of experiments for quality
engineering; statistical quality control; and acceptance sampling for quality control.
Prerequisite: STAT 313 or STAT 332
ESCI 450, Integrated Production/Manufacturing Systems, 3
Fundamental theories and techniques, decision making, and artificial intelligence for
solving production/manufacturing problems. Formulation, modeling, planning, and control of
production problems at three levels: strategic, tactical, and operational (long term, medium,
and short term). Specific problems include aggregate planning, project planning, scheduling,
line balancing, sequencing, and machine set-up. Special emphasis will be given on
decomposition and control of computer integrated systems, on-line and off-line supervisory
planning, and man/machine systems.
ESCI 463, Techniques of Model-based Control, 3
(Also listed as ECHE 463) Strategies of process control centered around the use of
process models in the control system. Topics include single loop, feed forward, cascade and
multi-variable internal model control. Tuning controllers to accommodate process
uncertainty. Treatment of control effect and output constraints in model predictive control
and modular-multivariable control
Prerequisite: ESCI 304
ESCI 509, Analysis of Discrete Event Systems, 3
Mathematical analysis of discrete event systems. Analytical frameworks used for
modeling and analysis including automata, queuing networks, algebra, and computer based
methods. Performance analysis using perturbation, spectral, and simulation techniques.
Discussion of current literature and research. A course project is required.
ESCI 515, Decision Theory with Applications, 3
Fundamentals of decision theory and analysis of decision processes in systems.
Elementary decision analysis. Single and multiattribute utility theory under both certainty
and uncertainty. Bayesian decision analysis. Sequential decision processes including
dynamic programming and Markov processes. Analysis of multi-person decision processes
and game theory as related to management decisions. Applications to large-scale systems
and to decision support systems.
ESCI 516, Large Scale Optimization, 3
Concepts and techniques for dealing with large optimization problems encountered in
designing large engineering structure, control of interconnected systems, pattern
recognition, and planning and operations of complex systems; partitioning, relaxation,
restriction, decomposition, approximation, and other problem simplification devices; specific
algorithms; potential use of parallel and symbolic computation; student seminars and
projects.
Prerequisite: ESCI 416
ESCI 518, Nonlinear Systems: Analysis and Control, 3
Mathematical preliminaries: differential equations and dynamical systems, differential
geometry and manifolds. Dynamical systems and feedback systems, existence and
uniqueness of solutions. Complicated dynamics and chaotic systems. Stability of nonlinear
systems: input-output methods and Lyapunov stability. Control of nonlinear systems: gain
scheduling, nonlinear regulator theory and feedback linearization.
Prerequisite: ESCI 408 and ESCI 421
ESCI 523, Multiobjective and Hierarchical Systems, 3
Basic concepts of hierarchical, multilevel systems. Lagrangian decompositions and
coordination principles. Fundamentals and recent advances in theory, methodology, and
applications of multiple criteria decision making (MCDM) with single and multiple decision
makers. Interactive MCDM methods. Multiple objectives for discrete and continuous models.
Multiobjective programming methods. Hierarchical overlapping coordination with single and
multiple objectives. Multiobjective multistage impact analysis. Applications to large scale
systems and to decision support systems.
ESCI 601, Independent Study, 1-36
ESCI 620, Special Topics, 1-36
ESCI 621, Special Projects, 1-36
ESCI 651, Thesis M.S., 1-36
ESCI 701, Dissertation Ph.D., 1-36
BACHELOR OF SCIENCE IN ENGINEERING DEGREE
MAJOR IN SYSTEMS AND CONTROL ENGINEERING
Fall Semester | Class/Lab/Credit
Hours | Spring Semester | Class/Lab/Credit Hours |
|
FRESHMAN |
| Open elective or humanities/social science | (3-0-3) | Humanities or social science/open elective | (3-0-3) |
CHEM 105, Principles of Chemistry I
or
CHEM 107, Properties
and Structure of Matter I | (3-0-3)
(3-0-3) | CHEM 106, Principles of Chemistry II
or
CHEM 108, Properties and
Structure of Matter II | (3-0-3)
(3-0-3) |
| CMPS 131, Elementary Computer Programming | (2-2-3) | CHEM 113, Principles of Chemistry Laboratory | (1-3-2) |
| MATH 121, Calculus for Science and Engineering I | (4-0-4) | MATH 122, Calculus for Science & Engineering
II | (4-0-4) |
| ENGL 150, Expository Writing | (3-0-3) | PHYS 121, General Physics I, Mechanics | (4-0-4) |
| PHED 101, Physical Education Activities | (0-3-0) | PHED 102, Physical Education Activities | (0-3-0) |
| Total | (15-5-16) | Total | (15-6-16) |
|
SOPHOMORE |
| Humanities or Social Science Sequence I | (3-0-3) | Humanities or Social Science Sequence II | (3-0-3) |
| ESCI 110, Problem Solving and Systems Engineering | (3-0-3) | MATH 224, Elementary Differential Equations | (3-0-3) |
| MATH 223, Calculus for Science and Engineering III | (3-0-3) | PHYS 221, General Physics III, Modern Physics | (3-0-3) |
| PHYS 122, General Physics II, Electricity & Magnetism | (4-0-4) | Engineering Core Elective | (3-0-3)
b |
| ESCI 352, Engineering Economics | (3-0-3) | STAT 312, Statistics for Engineering and Science
or
STAT 332,
Statistics for Signal Processing | (3-0-3)
(3-0-3) |
| ESCI 353, Accounting for Engineers (1-0-1) |
| Total | (17-0-17) | Total | (15-0-15) |
|
JUNIOR |
| Humanities or Social Science Sequence III | (3-0-3) | Humanities or Social Science Sequence IV | (3-0-3) |
| MATH 201, Linear Algebra | (3-0-3) | ESCI
313, Signal Processing | (3-0-3) |
| ESCI 212, Signals, Systems and Control | (3-0-3) | ESCI 304, Control Engineering I with lab | (2-3-3) |
| ESCI 214, Signals, Systems and Control Lab | (0-3-1) | ESCI 346, Engineering Optimization | (3-0-3) |
| ECMP 251, Numerical Methods | (2-2-3) b | ESCI 322, Simulation Methods in Engineering | (3-0-3) |
| Engineering Core Elective | (3-0-3) | ENGL
398, Professional Communication | (2-0-2) |
| Total | (14-5-16) | Total | (16-3-17) |
|
SENIOR |
| Humanities or social science elective | (3-0-3) | Humanities or social science elective | (3-0-3) |
| ESCI 398, Engineering Projects Laboratory I | (1-6-3) | Technical elective | (3-0-3) c |
| Technical elective | (3-0-3) c | Technical
elective | (3-0-3) c |
| Technical elective | (3-0-3) c | Open
elective | (3-0-3) |
| Technical elective | (3-0-3) c | ESCI 399,
Engineering Projects Laboratory II | (1-6-3) |
| Engineering Core Elective | (3-0-3) b |
| Total | (16-0-18) | Total | (13-6-15) |
Hours required for graduation: 130 plus graphics proficiency.
b Engineering Core course electives must be chosen from EMAE 150, Thermodynamics or
EMAE 171, Physical Chemistry I; EMAE 151, Fluid Mechanics or ECHE 360, Transport
Phenomena; ECIV 110, Mechanics; EMAE 181, Dynamics; EEAP 210, Fields/Energy
Conversion I; EEAP 240, Electronic Circuits I.
c Technical electives should be taken from the same elective sequence listed below. The
following lists of approved courses can be modified, with the approval of your advisor and
the department.
ESCI 306, Control Engineering II
EEAP 383, Microprocessor Applications to Control
ESCI 401, Digital Signal Processing
ESCI 509, Discrete Event Systems
ESCI 404, Digital Control Systems
ESCI 408, Introduction to Linear Systems
ESCI 416, Optimization Theory and Methods
Other approved courses in mathematics or engineering that are related to control systems
design and analysis.
ESCI 340, Introduction to Global Issues
ESCI 509, Discrete Event Systems
ESCI 414, Complex Systems Modeling and Analysis
ESCI 416, Optimization Theory and Methods
ESCI 427, Stochastic Methods for Engineers
OPRE 432, Computer Simulation
Other approved courses in operations research or engineering that are related to systems
analysis.
ESCI 250, Production Systems Engineering
ESCI 350, Manufacturing Systems or
ESCI 450, Integrated Production/Manufacturing Systems
ESCI 509, Discrete Event Systems
ESCI 427, Stochastic Methods for Engineers
OPRE 432, Computer Simulation
Other approved courses in operations management/operations research, materials or
engineering that are related to the analysis and control of industrial and manufacturing
systems.
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