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: These notes are in draft form and may contain errors. 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.
- 08 Jan 2022: updated version of Chapter 2 (trajectory generation) and full text (v2.2d)
- 21 Dec 2021: added new Chapter 1 (introduction) and starting to post updates for v2.2
- 15 Feb 2010: updated version of Chapter 4 (stochastic systems) is posted; multiple fixes + some new material
- 3 Jan 2010: posted updated version of Chapters 1 and 2 (mainly small fixes) + working errata link
- 22 Dec 2009: updated versions of all chapters posted; working through small fixes over the next few weeks
Contents
- Contents and Preface (PDF, 29 Dec 2021)
- Ch 1 - Introduction (PDF, 29 Dec 2021)
- System and Control Design
- The Control System “Standard Model”
- Layered Control Systems
- The Python Control Systems Library (python-control)
- Ch 2 - Trajectory Generation and Tracking (PDF, 29 Dec 2021)
- Two Degree of Freedom Design
- Trajectory Tracking and Gain Scheduling
- Trajectory Generation and Differential Flatness
- Ch 3 - Optimal Control (PDF, 27 Oct 2018)
- Review: Optimization
- Optimal Control of Systems
- Examples
- Linear Quadratic Regulators
- Choosing LQR Weights
- Advanced Topics
- Ch 4 - Receding Horizon Control (PDF, 22 Dec 2009)
- Optimization-Based Control
- Receding Horizon Control with CLF Terminal Cost
- Receding Horizon Control Using Differential Flatness
- Implementation on the Caltech Ducted Fan
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- Ch 5 - Stochastic Systems (PDF, 15 Feb 2010)
- Review of Random Variables
- Introduction to Random Processes
- Continuous-Time, Vector-Valued Random Processes
- Linear Stochastic Systems
- Random Processes in the Frequency Domain
- Ch 6 - Kalman Filtering (PDF, 22 Dec 2009)
- Linear Quadratic Estimators
- Extensions of the Kalman Filter
- LQG Control
- Application to a Vectored Thrust Aircraft
- Ch 7 - Sensor Fusion (PDF, 22 Dec 2009)
- Discrete-Time Stochastic Systems
- Kalman Filters in Discrete Time
- Predictor-Corrector Form
- Sensor Fusion
- Information Filters
- Additional Topics
- Bibliography and Index (PDF, 15 Feb 2010)
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