Supplement: Optimization-Based Control

Quick Links Complete text (PDF, 12 Mar 2023) Errata

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.

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

Contents and Preface (PDF, 29 Dec 2022) Ch 1 - Introduction (PDF, 29 Dec 2022) System and Control Design The Control System “Standard Model” Layered Control Systems The Python Control Systems Library (python-control): intro-iosys.ipynb (PDF), intro-xferfcn.ipynb (PDF) Ch 2 - Trajectory Generation and Tracking (PDF, 08 Jan 2022) Two Degree of Freedom Design Trajectory Tracking and Gain Scheduling Trajectory Generation and Differential Flatness Implementation in Python Other Methods for Generating Trajectories Ch 3 - Optimal Control (PDF, 19 Jan 2022) Review: Optimization Optimal Control of Systems Examples Linear Quadratic Regulators Choosing LQR Weights Advanced Topics Ch 4 - Receding Horizon Control (PDF, 31 Jan 2022) Optimization-Based Control Receding Horizon Control with CLF Terminal Cost Receding Horizon Control Using Differential Flatness Implementation on the Caltech Ducted Fan

Ch 5 - Stochastic Systems (PDF, 07 Feb 2022) 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, 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, 15 Feb 2010)