CDS 112/Ae 103a, Winter 2021

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Optimization Control and Estimation

Instructors

  • Richard Murray (CDS/BE), murray@cds.caltech.edu
  • Lectures: MWF, 2-3 pm, location TBD

Teaching Assistants

  • Ayush Pandey (CDS)
  • Office hours: TBD

This is the course homepage for CDS 112 (and Ae 103a), Winter 2021. This course is intended for undergraduates and graduate students interested in optimization-based methods in control. After completion of the course, students will understand the key principles of state-space based controller design, including optimal estimation and control techniques.

Catalog Description

CDS 112. Optimal Control and Estimation. 9 units (3-0-6): second term. Prerequisites: CDS 110 (or equivalent) and CDS 131. Optimization-based design of control systems, including optimal control and receding horizon control. Introductory random processes and optimal estimation. Kalman filtering and nonlinear filtering methods for autonomous systems.

Ae 103 a. Aerospace Control Systems.' 9 units (3-0-6): second term. Prerequisites: CDS 110 (or equivalent), CDS 131 or permission of instructor. Optimization-based design of control systems, including optimal control and receding horizon control. Introductory random processes and optimal estimation. Kalman filtering and nonlinear filtering methods for autonomous systems.

Lecture Schedule

Reading:

  • Opt = optional reading (useful if you are confused and trying to understand the basic concepts)
  • Rec = recommended reading (this is what the homework is based on)
  • Adv = advanced reading (more detailed results, useful if you are interested in learning more)

Announcements

  • 11 Oct 2021: Syllabus created
Date Topic Reading Homework
Week 1

3 Jan
5 Jan
7 Jan

Introduction and review
  • Course introduction and logistics
  • Review: TBD
  • Python Control Systems Library
HW #1

Out: 5 Jan
Due: 12 Jan

Week 2

10 Jan
12 Jan
19 Jan

Two degree of freedom control design
  • Trajectory generation
  • Differential flatness
  • OBC, Chapter 1
HW #2

Out: 12 Jan
Due: 19 Jan

Week 3

17 Jan
19 Jan
21 Jan

Optimal control
  • Maximum principle
  • Applications
  • OBC, Chapter 2
HW #3

Out: 19 Jan
Due: 26 Jan

Week 4

24 Jan
26 Jan
28 Jan*

Linear quadratic regulators
  • OBC, Chapter 2
HW #4

Out: 26 Jan
Due: 2 Feb

Week 5

31 Jan
2 Feb
4 Feb

Receding horizon control
  • OBC, Chapter 3
HW #5

Out: 2 Feb
Due: 9 Feb

Week 6

7 Feb
9 Feb
11 Feb

State estimation
  • FBS, Section 8.1-8.3
HW #6

Out: 9 Feb
Due: 16 Feb

Week 7

14 Feb
16 Feb
18 Feb

Stochastic systems
  • OBC, Chapter 4
HW #7

Out: 16 Feb
Due: 23 Feb

Week 8

21 Feb
23 Feb*
25 Feb*

Kalman filtering
  • OBC, Chapter 5
HW #8

Out: 23 Feb
Due: 2 Mar

Week 9

28 Feb
2 Mar
4 Mar

Sensor fusion
  • OBC, Chapter 6
HW #9

Out: 2 Mar
Due: 9 Mar

Week 10

7 Mar
9 Mar

Review for final Final

Out: 9 Mar
Due: 16 Mar, 5 pm

Grading

The final grade will be based on homework sets and a final exam:

  • Homework (70%): Homework sets will be handed out weekly and due on Wednesdays by 2 pm using GradeScope. Each student is allowed up to two extensions of no more than 2 days each over the course of the term. Homework turned in after Friday at 2 pm or after the two extensions are exhausted will not be accepted without a note from the health center or the Dean. MATLAB/Python code and SIMULINK/Modelica diagrams are considered part of your solution and should be printed and turned in with the problem set (whether the problem asks for it or not).
The lowest homework set grade will be dropped when computing your final grade.
  • Final exam (30%): The final exam will be handed out on the last day of class (4 Dec) and due at the end of finals week. It will be an open book exam and computers will be allowed (though not required).

Collaboration Policy

Collaboration on homework assignments is encouraged. You may consult outside reference materials, other students, the TA, or the instructor, but you cannot consult homework solutions from prior years and you must cite any use of material from outside references. All solutions that are handed in should be written up individually and should reflect your own understanding of the subject matter at the time of writing. Any computer code that is used to solve homework problems is considered part of your writeup and should be done individually (you can share ideas, but not code).

No collaboration is allowed on the final exam.

Course Text and References

The primary course texts are

Note: the only sources listed here are those that allow free access to online versions. Additional textbooks that are not freely available can be obtained from the library.