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Catalog data: Credit 3. Analysis and modeling of engineering systems including input-output and state-space descriptions. Root locus and frequency domain methods. Introduction to classical control design. Prerequisite: EE 221.
Textbook: N. S. Nise, Control Systems Engineering, J. Wiley, NY, NY, 2011.
References:
Instructor: Sami Fadali, Professor, EE (Office SEM 325 T, Th 10-11, W 4-5)
Goals: To introduce electrical engineering juniors to control system modeling, analysis & design.
Prerequisites by Topic: Mechanics, Circuits, Laplace Transform, Differential Equations.
Catalog data: Credit 3. State-space models, controllability, observability, classical design using frequency response and root locus, state feedback, observer design. Prerequisite: EE 370 or ME 410.
Textbook: M. Sami Fadali and A. Visioli, Digital Control Engineering, Academic Press, Burlington, MA, 2013.
References:
Instructor: Sami Fadali, Professor, EE
Grading:
Prerequisites: EE 370 Control Systems I
Catalog data: Credit 3. Difference equations and the z-transform, digital control system modeling, digital controller design, introduction to state-space methods. Prerequisite: EE 370.
Textbook: M. Sami Fadali and A. Visioli, Digital Control Engineering, Academic Press, Burlington, MA, 2013.
References:
Instructor: Sami Fadali, Professor, EE (Office SEM 325 T, Th 10-11, W 4-5)
Goals: To introduce electrical engineering seniors to digital control system modeling, analysis & design.
Prerequisites by Topic: Root locus, Frequency Response, Linear Algebra.
Student Learning Outcomes ( ABET a, b, c, d, e, g, k): Upon completing the course the student should be able to:
Topics (1 class = 75 min.):
Computer Usage: Students are expected to use the CAD package MATLAB extensively throughout the course as part of their weekly homework assignments.
Laboratory Projects: (3 classes) Position control system design. A written report is required for the project (b, g).
Graduate students registered in EE672 must complete a Digital Control research project.
Catalog data: Credit 3. Random variables and random signals, auto-correlation and cross-correlation functions. Power spectral density functions, minimum mean-squared estimation, maximum likelihood estimation, linear and extended Kalman filtering.
Textbook: R. G. Brown and P. Y. C. Hwang, Introduction to Random Signals and Applied Kalman Filtering, 4th, J. Wiley, NY, 2012.
References:
Instructor: Dr. M. S. Fadali, SEM 325
Journals:IEEE Trans. Signal Processing, IEEE Trans. Acoustics Speech Signal Processing, IEEE Trans. Automatic control, Automatica, International J. Control.Prerequisites: EE 362 Signals and Systems
Catalog data: Credit 3. Nonlinear state equations, phase plane analysis, describing function, Liapunov stability, circle criterion, introduction to nonlinear control systems design.
Textbook: H. J. Marquez, , Nonlinear Control Systems: Analysis and Design, J. Wiley, Hoboken, N.J. 2003.
References:
Instructor: Sami Fadali, Professor, EE
Journals: IEEE Transactions on Automatic Control, Circuits & Systems, International J. of Control, Automatica.
Grading: 40% Assignments 25% Project, 35 % Exams. Each student must complete a project on nonlinear control, including simulation or experimental results as part of the course. Each student must submit a monthly progress report and a final report and must give a fifteen minute oral presentation as part of the project.
Prerequisites: EE 381 Signals and Systems, Math 352 Probability and Statistics
Textbook: R. D. Hippenstiel, Detection Theory: Applications and Digital Signal Processing, CRC Press, Boca Raton, 2001.
References:
Instructor: Sami Fadali, Professor, EE
Journals: IEEE Trans. Signal Processing, IEEE Trans. Communication, IEEE Trans. Acoustics Speech Signal Processing, IEEE Trans. Automatic Control, Automatica, International J. Control.
Prerequisites by Topic: Probability and statistics, matrix algebra, state-space equations (basics)