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Computing That Serves

CS 513

Course Offerings

Section # Semester Instructor Website Description
1 Winter 2018 Sean Warnick
1 Fall 2016 Sean Warnick https://learningsuite.byu.edu/view/4FB4ANZ4bdbq.html
1 Fall 2015 Sean Warnick https://learningsuite.byu.edu/view/jGP1nDTI7LuY.html

Short Summary: 

Robust Control

Credits: 

3

Prerequisites: 

Robust Control

 

Overview: Design of algorithms to make decisions in complex, uncertain environments. Prerequisites: Math 343. CS 312, 412, and Math 334 are recommended.

 

 

This document is not a syllabus. Instead, for ALL offerings of this course, this document states the expected objectives and topics for the course. Faculty members teaching this course should adhere to these objectives and topics. Students taking this course can expect to achieve the objectives and cover the topics specified, and faculty members teaching follow-on courses can expect students to have been appropriately exposed to the prerequisite material as stated. The “hours” for topics listed below reflect the approximate number of 50-minute class periods (or equivalent) devoted to each topic.

 

Purpose

CS 613

This course lays the mathematical foundations for a thorough understanding of robust control theory. State space systems theory and linear analysis are covered in detail. We discuss model realizations and reduction, the Youla parameterization of all stabilizing controllers, and an overview of H2 and Hinfinity optimal control as solutions to convex optimization problems, with an emphasis on the role of Linear Martix Inequalities in these formulations.

Learning Outcomes

At the end of this course, students should be able to:

  • Understand system concepts of stability, controllability, observability, minimality, behavior, and structure.
  • Understand various mathematical representations of systems.
  • Understand the four basic problems: Model Reduction (i.e. Approximation), System Identification (i.e. Learning), Control (i.e. Decision Making), and Verification, and how they impact each other.
  • Understand Hankel Singular Values and their role in balanced truncation as an approximation to optimal model reduction.
  • Understand Linear Matrix Inequalities and their role in computing solutions to various identification and control problems.
  • Understand the role of the Small Gain Theorem and Passivity in providing guarantees of algorithm correctness in spite of model uncertainty.

    Prerequisite(s): CS 312 & 412; Math 334; or equivalent.