Control Systems

Control systems are the critical center of any vehicle system. Examples of control systems are numerous and multifaceted: climate control for passenger comfort in an automobile, automatic cruise control, engine control and pollution control are some typical illustrations. Design of control systems for practical applications requires a through understanding of physical models of the process, mathematical modeling techniques, transient behavior of systems and dynamic characteristics of a physical system.

The Control Systems certificate program will introduce the participants to mathematical techniques of system analysis, use of software, such as Matlab, to enhance the student’s experience, system modeling, continuous and discrete time control techniques, including analog and digital PID controllers, digital control, fuzzy logic control, neural network controller, etc. At the next level, participants will be introduced to multivariable control (control of several interacting variables of a physical process) and design strategies for multivariable processes. Finally, the program will introduce some basic concepts in nonlinear control and simple design techniques. Several case studies will be presented to enhance the learning experience. Group design projects will be assigned to ensure that the participants understand the design process. (12 credit hours) 

Coursework

Please choose four courses to complete the required 12 credit hours.
ECE 512Analog Filter Design3
ECE 552Fuzzy Systems3
ECE 560Modern Control Theory3
ECE 565Digital Control Systems3
ECE 567Nonlinear Control Systems3
ECE 583Artificial Neural Networks3

ECE 512     Analog Filter Design     3 Credit Hours

This course addresses the analysis and design of continuous time (analog) and switched-capacitor filters. Students will analyze and design filters. Effect of tolerances of circuit elements on the performance of the circuit behavior will be analyzed. Students will use simulation tools to design filters and verify circuit performance. Three lecture hours per week.

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

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 560     Modern Control Theory     3 Credit Hours

Introduction to linear spaces and operators; mathematical description of multiple input-output systems; state variables and state transition matrix; controllability and observability and its application to irreducible realization of transfer function matrices; state variable estimation; controller synthesis by state feedback; stability of linear systems; analysis of composite systems.

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

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 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