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This is a 2-semester course for engineering students in Electrical, Computer and Systems Engineering. The topics covered include basic linear system theory, introduction to regularized methods in linear systems theory, zero-forcing methods, projection algorithms for L^2(R) and R spaces, maximum a posteriori detection algorithm with noise-power constraint in the presence of jammers, matching pursuit filtering in frequency-wavenumber domain, nonparametric multistatic array beamforming with partial transmit covariance information, diffusion filters in the frequency domain and optimization of their parameters.

The “textbook” is now the textbook. Instructionally, by far, this is the best approach to teaching an introductory course. I can learn at my own pace; review what seems most difficult, rewind the video (see below); turn on closed captions (to see what’s behind “the math”).

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**SUPPLEMENTS**

**Companion website** at www.oup.com/us/chen contains PowerPoint-based versions of the figures from the text (available to adopters of the text)

**An Instructor’s Solutions Manual** is available to adopters

## Table of Contents for Linear System Theory And Design Solution Manual pdf

Preface

1. Introduction

1.1. Introduction

1.2. Overview

2. Mathematical Descriptions of Systems

2.1. Introduction

2.2. Causality, Lumpedness, and Time-Invariance

2.3. Linear Time-Invariant (LTI) Systems

2.4. Linear Time-Varying Systems

2.5. RLC circuits–Comparisons of Various Descriptions

2.6. Mechanical and Hydraulic Systems

2.7. Proper Rational Transfer Functions

2.8. Discrete-Time Linear Time-Invariant Systems

2.9. Concluding Remarks

3. Linear Algebra

3.1. Introduction

3.2. Basis, Representation, and Orthonormalization

3.3. Linear Algebraic Equations

3.4. Similarity Transformation

3.5. Diagonal Form and Jordan Form

3.6. Functions of a Square Matrix

3.7. Lyapunov Equation

3.8. Some Useful Formula

3.9. Quadratic Form and Positive Definiteness

3.10. Singular Value Decomposition

3.11. Norms of Matrices

4. State-Space Solutions and Realizations

4.1. Introduction

4.2. General Solution of CT LTI State-Space Equations

4.3. Computer Computation of CT State-Space Equations

4.4. Equivalent State Equations

4.5. Realizations

4.6. Solution of Linear Time-Varying (LTV) Equations

4.7. Equivalent Time-Varying Equations

4.8. Time-Varying Realizations

5. Stability

5.1. Introduction

5.2. Input-Output Stability of LTI Systems

5.3. Discrete-Time Case

5.4. Internal Stability

5.5. Lyapunov Theorem

5.6. Stability of LTV Systems

6. Controllability and Observability

6.1. Introduction

6.2. Controllability

6.3. Observability

6.4. Canonical Decomposition

6.5. Conditions in Jordan-Form Equations

6.6. Discrete-Time State-Space Equations

6.7. Controllability After Sampling

6.8. LTV State-Space Equations

7. Minimal Realizations and Coprime Fractions

7.1. Introduction

7.2. Implications of Coprimeness

7.3. Computing Coprime Fractions

7.4. Balanced Realization

7.5. Realizations from Markov Parameters

7.6. Degree of Transfer Matrices

7.7. Minimal Realizations- Matrix Case

7.8. Matrix Polynomial Fractions

7.9. Realization from Matrix Coprime Fractions

7.10. Realizations from Matrix Markov Parameters

7.11. Concluding Remarks

8. State Feedback and State Estimators

8.1. Introduction

8.2. State Feedback

8.3. Regulation and Tracking

8.4. State Estimator

8.5. Feedback from Estimated States

8.6. State feedback–MIMO case

8.7. State Estimators–MIMO case

8.8. Feedback from Estimated States–MIMO Case

9. Pole Placement and Model Matching

9.1. Introduction

9.2. Preliminary–Matching Coefficients

9.3. Unity-Feedback Configuration-Pole Placement

9.4. Implementable Transfer Functions

9.5. MIMO Unity Feedback Systems

9.6. MIMO Model Matching–Two-Parameter Configuration

9.7. Concluding Remarks

References

Answers to Selected Problems

Index