ECE 415: System Modeling, Identification and Simulation

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ECE 415 - System Modeling, Identification and Simulation

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ECE Undergraduate Curriculum in Control Systems

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Catalog Description

415 System Modeling, Identification and Simulation. Irr.; 3 cr (P-I). Principles of mathematical modeling of linear and nonlinear, continuous and discrete systems. Real-time computer-assisted simulation and identification of engineering systems (electrical, mechanical, hydraulic, acoustic, etc.). Methods of on-line and off-line system identification. Introduction to the behavior of forced and unforced nonlinear dynamic systems. P: Comp Sci 312 or 412, ECE 330, Math 340.

Course Prerequisite(s)

ECE 330
Comp Sci 312
Comp Sci 412
Math 340

Prerequisite knowledge and/or skills

Familiarity with Laplace transform techniques
Ability to program in a high level language (C, C++, Pascal, Fortran, or interpreted mathematical environments such as MATLAB, Mathematica)
Knowlege of basic linear algebra; vector/matrix manipulation.

Textbook(s) and/or other required material

Course readings are assembled from selected papers and articles.

Course objectives

The course begins by discussing several different representations of system and their behavior. The second part of the course deals with algorithms to determine numerical values for free parameters within a given model structure:

1. hypothesize a model of the system, 2. calculate an estimated output, 3. compare the estimated output to the actual output, and 4. use the error

to improve the accuracy with which the model predicts the observed behavior of the actual system.

Topics covered

1. Representation of systems: electrical/mechanical/hydraulic/acoustic; transfer function vs. state space representation, numerical integration using ACSL, numerical methods and discretization. 2. Linear systems: methods of model order determination, impulse and frequency response methods. 3. Time varying (linear) systems: stability concepts, fractal behavior. 4. Nonlinear models: introduction to stable oscillations, chaotic behavior, jump phenomena. 5. Linear modeling, identification and simulation: least squares identification methods (off line) using MATLAB; on-line, recursive, "real time" identification using ACSL. 6. Linear modeling, identification and simulation: applications of LS and ARMA methods. 7. Linear modeling, identification and simulation: regression methods. 8. Nonlinear modeling, identification and simulation: nonlinear models I & II; examples. 9. Nonlinear modeling, identification and simulation: methods of nonlinear identification I & II; examples.

Class/laboratory schedule

Three 50 minute lectures or two 75 minute lectures per week; scheduling varies with semester.

Contribution of course to meeting the professional component

This course contributes primarily to the students' knowledge of engineering topics, and does provide design experience.

The following statement indicates which of the following considerations are included in this course: economic, environmental, ethical, political, societal, health and safety, manufacturability, sustainability.

The end of semester project experience exposes students to economic, environmental, ethical, societal, manufacturability, sustainability issues in design.

Relationship of course to undergraduate degree program objectives and outcomes

This course primarily serves students in the department. The information below describes how the course contributes to the undergraduate program objectives.

knowledge in the basic techniques of mathematics and the physical sciences basic skill in methods of design and analysis across a broad range of electrical and computer engineering areas

Assessment of student progress toward course objectives

Assessment of student learning is through a mid-term exam, and sequence of design oriented homework assignments culminating in a significant, student slected end of semester project. Students are expected to draw on out of classroom resources (articles in professional journals, co-op industry experience, etc.) in selecting their system modelling and identification project.

Person(s) who prepared this description

William A. Sethares

Copyright 2007 The Board of Regents of the University of Wisconsin System
Date last modified: 18-Jul-2007
Content by: ece@engr.wisc.edu
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