Intelligent Control

Intelligent Controls deals with the application of artificial intelligence, knowledge base, expert systems fuzzy logic and/ or neural networks for controlling complex physical processes that are difficult to control using conventional methods. Examples of such processes include vehicle control, autonomous vehicles, automatic control, glass furnaces, etc. Participants will be introduced to basic concepts and issues in control systems. This will be followed by a discussion of strategies for conventional controller design. Examples of such controllers include PID control, state variable feedback, etc. Case studies where such controllers are appropriate will be presented. The next phase of the program will deal with the design of non-linear controllers. These will be discussed as alternatives to conventional controllers. Group projects will be assigned to participants.

The final phase of this program will address the application of knowledge base systems and expert systems in controlling very complex processes that are not easily controlled by conventional methods, including neural networks. The emphasis will be on capturing human expertise and translating this expertise into a set of rules for facilitating effective control. Case studies will be utilized to enhance the quality of this subject matter. (12 credit hours)

Certificate offered on Campus and via Distance Learning 

Coursework

Complete 4 courses from the following (12 credits):
ECE 505Intro to Embedded Systems3
ECE 532Auto Sensors and Actuators3
ECE 552Fuzzy Systems3
ECE 565Digital Control Systems3
ECE 567Nonlinear Control Systems3
ECE 576Information Engineering3
ECE 579Intelligent Systems3
ECE 583Artificial Neural Networks3

ECE 505     Intro to Embedded Systems     3 Credit Hours

Introduction to modern digital computer logic. Numbers and coding systems; Boolean algebra with application to logic systems; examples of digital logic circuits; simple machine language programming and Assembly and C/C+ programming language; microprocessors programming (both assembly and C/C+) for input/output, interrupts, and system design. (May not be available to students with EE or CE degrees) Three lecture hours per week.

Restriction(s):
Can enroll if Class is Graduate
Can enroll if Major is Electrical Engineering, Computer Engineering

ECE 532     Auto Sensors and Actuators     3 Credit Hours

Study of automotive sensory requirements; types of sensors; available sensors and future needs. Study of functions and types of actuators in automotive systems. Dynamic models of sensors and actuators. Integrated smart sensors and actuators. Term project.

Restriction(s):
Can enroll if Class is Graduate

ECE 552     Fuzzy Systems     3 Credit Hours

A study of the concept of fuzzy set theory including operations on fuzzy sets, fuzzy relations, fuzzy measures, fuzzy logic, with an emphasis on engineering application. Topics include fuzzy set theory, applications to image processing, pattern recognition, artificial intelligence, computer hardware design, and control systems.

Restriction(s):
Can enroll if Class is Graduate

ECE 565     Digital Control Systems     3 Credit Hours

Mathematical representation of digital control systems; z-transform and difference equations; classical and state space methods of analysis and design; direct digital control of industrial processes.

Restriction(s):
Can enroll if Class is Graduate

ECE 567     Nonlinear Control Systems     3 Credit Hours

Nonlinearities in control systems; phase plane analysis; isoclines, equilibrium points, limit cycles, optimum systems; heuristic methods; harmonic balance, describing function, frequency response and jump phenomena, oscillations in relay systems; state space; optimum relay controls; stability; Liapunov's method.

Restriction(s):
Can enroll if Class is Graduate

ECE 576     Information Engineering     3 Credit Hours

This course will cover fundamental concepts of information engineering, including theoretical concepts of how information is measured and transmitted, how information is structured and stored, how information can be compressed and decompressed, and information networks such as social networks, affiliation networks and online networks, mathematical theories of information networks. Information engineering applications will be discussed. Three lecture hours per week.

Restriction(s):
Can enroll if Class is Graduate
Can enroll if Level is Doctorate or Rackham or Graduate
Can enroll if Major is Computer Engineering, Software Engineering, Electrical Engineering, Computer & Information Science

ECE 579     Intelligent Systems     3 Credit Hours

Representative topics include: Intelligent systems design, training and evaluation, decision trees, Bayesian learning, reinforcement learning. A project will be required.

Restriction(s):
Can enroll if Level is Rackham or Graduate or Doctorate
Can enroll if Major is Computer & Information Science, Software Engineering, Electrical Engineering, Computer Engineering

ECE 583     Artificial Neural Networks     3 Credit Hours

Students will gain an understanding of the language, formalism, and methods of artificial neural networks. The student will learn how to mathematically pose the machine learning problems of function approximation/supervised learning, associative memory and self-organization, and analytically derive some well-known learning rules, including backprop. The course will cover computer simulations of various neural network models and simulations. Three lecture hours per week.

Restriction(s):
Can enroll if Class is Graduate
Can enroll if Level is Graduate or Rackham or Doctorate
Can enroll if Major is Computer Engineering, Electrical Engineering, Computer & Information Science, Software Engineering

 
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An asterisk denotes that a course may be taken concurrently.

Frequency of Offering

The following abbreviations are used to denote the frequency of offering: (F) fall term; (W) winter term; (S) summer term; (F, W) fall and winter terms; (YR) once a year; (AY) alternating years; (OC) offered occasionally