Sinopsis
This book introduces the theory and practice of modern control systems. The emphasis is on using state-space methods to model, analyze, and control dynamic systems. Topics include state-space modeling and solutions, stability, controllability and observability, state-feedback control, observers, observer state feedback controls, least square estimation, Kalman filter, and Linear Quadratic Gaussian optimal control. These topics are discussed in both continuous- and discrete-time settings throughout the book. Perhaps unique in this book is the modernization of tools we use to illustrate controls. MATLAB and Python are the primary tools for our numerical examples. Whenever MATLAB examples appear, complementary Python codes will follow to provide results as equivalent as possible in the more nascent computation environment. Free slides, codes, and lecture recordings supplement the text.
Table of Contents:
1 Introduction
PART I: System Description
2 Modeling
3 Laplace and Z Transforms
4 State-Space Description of a Dynamic System
5 State-space Realizations: the Canonical Forms
6 Solution of Time-Invariant State-Space Equations
7 Discrete-Time Models of Continuous Systems
PART II: System Properties
8 Stability
9 Controllability and Observability
10 Kalman Decomposition
PART III: Estimation and Control
11 State Feedback
12 Observers and Observer-State Feedback
13 Linear Quadratic Optimal Control
PART IV: Stochastic Estimation and Control
14 Review of Probability Theory
15 Least Square Estimation
16 Stochastic State Estimation and Kalman Filter
17 Linear Quadratic Gaussian (LQG) Optimal Control
18 Further Readings
Appendix: Review of Relevant Linear Algebra
Appendix: How to Install and Run Python
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