Difference between revisions of "Supplement: Optimization-Based Control"

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** Implementation on the Caltech Ducted Fan
 
** Implementation on the Caltech Ducted Fan
 
| width=50% |
 
| width=50% |
* {{OBC pdf|Ch 5 - Stochastic Systems|obc-stochastic|07Feb2022}}
+
* {{OBC pdf|Ch 5 - Stochastic Systems|obc-stochastic|05Feb2023}}
** Review of Random Variables
+
** Brief Review of Random Variables
 
** Introduction to Random Processes
 
** Introduction to Random Processes
 
** Continuous-Time, Vector-Valued Random Processes
 
** Continuous-Time, Vector-Valued Random Processes
** Linear Stochastic Systems
+
** Linear Stochastic Systems with Gaussian Noise
 
** Random Processes in the Frequency Domain
 
** Random Processes in the Frequency Domain
 +
** Implementation in Python: {{OBC notebook|stochastic-linsys}}
 
* {{OBC pdf|Ch 6 - Kalman Filtering|obc-kalman|13Feb2022}}
 
* {{OBC pdf|Ch 6 - Kalman Filtering|obc-kalman|13Feb2022}}
 
** Linear Quadratic Estimators
 
** Linear Quadratic Estimators

Revision as of 06:43, 6 February 2023

Quick Links

Richard M. Murray

These notes serve as a supplement to Feedback Systems by Åström and Murray and expand on some of the topics introduced there. Our focus is on the use of optimization-based methods for control, including optimal control theory, receding horizon control, and Kalman filtering. Each chapter is intended to be a standalone reference for advanced topics that are introduced in Feedback Systems.

Note: Permission is granted to download and print a copy for individual use, but this material may not be reproduced, in whole or in part, without written consent from the author.

News (archive)
  • Jan-Feb 2023: updated versions of Chapters 2-7 (version 2.3x) posted roughly every week
  • 31 Dec 2022: posted Jupyter notebooks for Chapter 1 (intro to python-control)
  • 29 Dec 2022: added new Chapter 1 (introduction) and starting to post updates for v2.3

Contents

  • Ch 5 - Stochastic Systems (PDF, 05 Feb 2023)
    • Brief Review of Random Variables
    • Introduction to Random Processes
    • Continuous-Time, Vector-Valued Random Processes
    • Linear Stochastic Systems with Gaussian Noise
    • Random Processes in the Frequency Domain
    • Implementation in Python: stochastic-linsys.ipynb (PDF)
  • Ch 6 - Kalman Filtering (PDF, 13 Feb 2022)
    • Linear Quadratic Estimators
    • Extensions of the Kalman Filter
    • LQG Control
    • Application to a Vectored Thrust Aircraft
  • Ch 7 - Sensor Fusion (PDF, 23 Feb 2022)
    • Discrete-Time Stochastic Systems
    • Kalman Filters in Discrete Time
    • Predictor-Corrector Form
    • Sensor Fusion
    • Information Filters
    • Additional Topics
  • Bibliography and Index (PDF, 03 Jan 2023)