Difference between revisions of "CDS 112/Ae 103a, Winter 2022"
Line 52:  Line 52:  
* Trajectory generation  * Trajectory generation  
* Differential flatness  * Differential flatness  
* Implementation in Python  
* Gain scheduling (if time)  * Gain scheduling (if time)  
    
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* Maximum principle  * Maximum principle  
* Applications  * Applications  
* Implementation in Python  
    
* OBC, Chapter 3  * OBC, Chapter 3  
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24 Jan <br> 26 Jan <br> 28 Jan*  24 Jan <br> 26 Jan <br> 28 Jan*  
 Linear quadratic regulators   Linear quadratic regulators  
* Problem formulation and solution  
* Choosing LQR Weights  
* Incorporating integral feedback  
* Implementation in Python  
    
* OBC, Chapter 3  * OBC, Chapter 3  
Line 90:  Line 96:  
31 Jan <br> 2 Feb <br> 4 Feb  31 Jan <br> 2 Feb <br> 4 Feb  
 Receding horizon control   Receding horizon control  
* Problem formulation and solution  
* Receding horizon control using differential flatness  
* Example: Caltech ducted fan  
* Implementation in Python  
    
* OBC, Chapter 4  * OBC, Chapter 4  
Line 101:  Line 111:  
7 Feb <br> 9 Feb <br> 11 Feb  7 Feb <br> 9 Feb <br> 11 Feb  
 Stochastic systems   Stochastic systems  
* Review of random variables  
* Introduction to random processes  
* Continuoustime, vectorvalued random processes  
* Linear stochastic systems  
* Random processes in the frequency domain  
    
* OBC, Chapter 5  * OBC, Chapter 5  
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14 Feb <br> 16 Feb <br> 18 Feb*  14 Feb <br> 16 Feb <br> 18 Feb*  
 Kalman filtering   Kalman filtering  
* Linear quadratic estimators  
* Extensions of the Kalman filter  
* LQG control  
* Example: vectored thrust aircraft  
* Implementation in Python  
    
* OBC, Chapter 6  * OBC, Chapter 6  
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<s>21 Feb</s> <br> 23 Feb* <br> 25 Feb*  <s>21 Feb</s> <br> 23 Feb* <br> 25 Feb*  
 Sensor fusion   Sensor fusion  
* Discretetime stochastic systems  
* Kalman filters in discrete time  
* Predictorcorrector form  
* Combining information from multiple sensors  
* Information filters  
* Implementation in Python  
    
* OBC, Chapter 7  * OBC, Chapter 7  
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28 Feb <br> 2 Mar <br> 4 Mar  28 Feb <br> 2 Mar <br> 4 Mar  
 Autonomous systems   Autonomous systems  
* Multilayer control stack for autonomous systems  
* Introduction to discrete decisionmaking  
* Introduction to safetycritical systems  
* Challenges and open problems  
    
* TBD  * TBD 
Revision as of 02:40, 25 December 2021
Optimal Control and Estimation  
Instructors

Teaching Assistants

This is the course homepage for CDS 112 (and Ae 103a), Winter 2022. This course is intended for undergraduates and graduate students interested in optimizationbased methods in control. After completion of the course, students will understand the key principles of statespace based controller design, including optimal estimation and control techniques.
Catalog Description
CDS 112. Optimal Control and Estimation. 9 units (306): second term. Prerequisites: CDS 110 (or equivalent) and CDS 131. Optimizationbased 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 (306): second term. Prerequisites: CDS 110 (or equivalent), CDS 131 or permission of instructor. Optimizationbased 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
Date  Topic  Reading  Homework 
Week 1 3 Jan 
Introduction and review


Template:Cds112 wi2021 pdf Out: 5 Jan 
Week 2 10 Jan 
Two degree of freedom control design


Template:Cds112 wi2021 pdf Out: 12 Jan 
Week 3

Optimal control


Template:Cds112 wi2021 pdf Out: 19 Jan 
Week 4 24 Jan 
Linear quadratic regulators


Template:Cds112 wi2021 pdf Out: 26 Jan 
Week 5 31 Jan 
Receding horizon control


Template:Cds112 wi2021 pdf Out: 2 Feb 
Week 6 7 Feb 
Stochastic systems


Template:Cds112 wi2021 pdf Out: 9 Feb 
Week 7 14 Feb 
Kalman filtering


Template:Cds112 wi2021 pdf Out: 16 Feb 
Week 8

Sensor fusion


Template:Cds112 wi2021 pdf Out: 23 Feb 
Week 9 28 Feb 
Autonomous systems


Template:Cds112 wi2021 pdf Out: 2 Mar 
Week 10 7 Mar 
Review for final  Template:Cds112 wi2021 pdf Out: 9 Mar 
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
 [OBC] R. M. Murray, "OptimizationBased Control", 2022. Online access
The following additional references may also be useful:
 [FBS2e] K. J. Astrom and Richard M. Murray, Feedback Systems: An Introduction for Scientists and Engineers, Princeton University Press, Second Edition*, 2020.
 [LST] Richard M. Murray, Feedback Systems: Notes on Linear Systems Theory, 2020. (Updated 30 Oct 2020)
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.