Control Systems
About the Program
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)
The certificate can be completed entirely on campus, entirely online, or through a combination of on-campus and online courses.
Course Descriptions
Please choose four courses to complete the required 12 credit hours.
Introduction to linear spaces and operators; mathematical description 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 syntheses by state feedback; stability of linear systems; analysis of composite systems. (3 credits)
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. (3 credits)
A study of 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, and Liapunov’s method are also covered. (3 credits)
Students will gain an understanding of the language, formalism, and methods of pattern recognition. Various solution approaches will be covered including statistical methods and neural network-based methods. Students will learn how to mathematically pose various pattern recognition problems and analytically derive some well-known statistical results and learning rules. In addition, the student will learn how to perform computer simulations of various statistical and neural network models and learn how to select appropriate model parameters, such as network architecture, hidden layer size, and learning rate. Case Studies will be presented to illustrate a variety of applications. (3 credits)
A study of the concept of fuzzy set theory including operations on fuzzy sets, fuzzy relations, fuzzy measures, and fuzzy logic, with an emphasis on engineering applications. Topics include fuzzy set theory, application to image processing, pattern recognition, artificial intelligence, computer hardware design and control systems. (3 credits)
This course deals with the analysis and design of continuous-time (analog) and switched capacitor filters. Students will learn how to analyze and design analog filter, whether they are passive, active or switched-capacitor filters. Effect of tolerances of circuit elements on the performance of the circuit behavior will be discussed. Also, students will learn how to use simulation tools to design filters and verify circuit performance. (3 credits)
Learning Goals and Outcomes
- Students will know how to complete and partake in group design projects.
- Enhance the experience, system modeling, continuous and discrete time control techniques, including analog and digital PID controllers, digital control, fuzzy logic control, neural network controller, etc.
- Students will be introduced to multivariable control (control of several interacting variables of a physical process) and design strategies for multivariable processes.
Admission Requirements
Applicants must possess an undergraduate degree in Electrical Engineering with an overall GPA of 3.0 or higher.
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ECE 560 |
Fall & Winter |
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ECE 565 |
Fall |
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ECE 567 |
Winter |
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ECE 5831 |
Fall |
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ECE 552 |
Fall |
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ECE 5121 |
Bi-Fall |