**Simulation of Dynamic Systems with MATLAB and Simulink 3rd Edition by Harold Klee, ISBN-13: 978-1498787772**

[PDF eBook eTextbook]

- Publisher: CRC Press; 3rd edition (November 21, 2017)
- Language: English
- 852 pages
- ISBN-10: 9781498787772
- ISBN-13: 978-1498787772

Continuous-system simulation is an increasingly important tool for optimizing the performance of real-world systems. The book presents an integrated treatment of continuous simulation with all the background and essential prerequisites in one setting. It features updated chapters and two new sections on Black Swan and the Stochastic Information Packet (SIP) and Stochastic Library Units with Relationships Preserved (SLURP) Standard. The new edition includes basic concepts, mathematical tools, and the common principles of various simulation models for different phenomena, as well as an abundance of case studies, real-world examples, homework problems, and equations to develop a practical understanding of concepts.

**Table of Contents:**

Half Title

Title Page

Copyright Page

Dedication

Contents

Foreword

Preface

About the Authors

Chapter 1: Mathematical Modeling

1.1 Introduction

1.1.1 Importance of Models

1.2 Derivation of A Mathematical Model

1.3 Difference Equations

1.4 First Look at Discrete-Time Systems

1.4.1 Inherently Discrete-Time Systems

1.5 Case Study: Population Dynamics (Single Species)

Chapter 2: Continuous-Time Systems

2.1 Introduction

2.2 First-Order Systems

2.2.1 Step Response of First-Order Systems

2.3 Second-Order Systems

2.3.1 Conversion of Two First-Order Equations to a Second-Order Model

2.4 Simulation Diagrams

2.4.1 Systems of Equations

2.5 Higher-Order Systems

2.6 State Variables

2.6.1 Conversion from Linear State Variable Form to Single Input–Single Output Form

2.6.2 General Solution of the State Equations

2.7 Nonlinear Systems

2.7.1 Friction

2.7.2 Dead Zone and Saturation

2.7.3 Backlash

2.7.4 Hysteresis

2.7.5 Quantization

2.7.6 Sustained Oscillations and Limit Cycles

2.8 Case Study: Submarine Depth Control System

Chapter 3: Elementary Numerical Integration

3.1 Introduction

3.2 Discrete-Time System Approximation of a Continuous First-Order System

3.3 Euler Integration

3.3.1 Explicit Euler Integration

3.3.2 Implicit Euler Integration

3.4 Trapezoidal Integration

3.5 Discrete Approximation of Nonlinear First-Order Systems

3.6 Discrete State Equations

3.7 Improvements to Euler Integration

3.7.1 Improved Euler Integration

3.7.2 Modified Euler Integration

3.7.3 Discrete-Time System Matrices

3.8 Case Study: Vertical Ascent of a Diver

Chapter 4: Linear Systems Analysis

4.1 Introduction

4.2 Laplace Transform

4.2.1 Properties of the Laplace Transform

4.2.2 Inverse Laplace Transform

4.2.3 Laplace Transform of the System Response

4.2.4 Partial Fraction Expansion

4.3 Transfer Function

4.3.1 Impulse Function

4.3.2 Relationship between Unit Step Function and Unit Impulse Function

4.3.3 Impulse Response

4.3.4 Relationship between Impulse Response and Transfer Function

4.3.5 Systems with Multiple Inputs and Outputs

4.3.6 Transformation from State Variable Model to Transfer Function

4.4 Stability of Linear Time Invariant Continuous-Time Systems

4.4.1 Characteristic Polynomial

4.4.2 Feedback Control System

4.5 Frequency Response of LTI Continuous-Time Systems

4.5.1 Stability of Linear Feedback Control Systems Based on Frequency Response

4.6 z-Transform

4.6.1 Discrete-Time Impulse Function

4.6.2 Inverse z-Transform

4.6.3 Partial Fraction Expansion

4.7 z-Domain Transfer Function

4.7.1 Nonzero Initial Conditions

4.7.2 Approximating Continuous-Time System Transfer Functions

4.7.3 Simulation Diagrams and State Variables

4.7.4 Solution of Linear Discrete-Time State Equations

4.7.5 Weighting Sequence (Impulse Response Function)

4.8 Stability of LTI Discrete-Time Systems

4.8.1 Complex Poles of H(z)

4.9 Frequency Response of Discrete-Time Systems

4.9.1 Steady-State Sinusoidal Response

4.9.2 Properties of the Discrete-Time Frequency Response Function

4.9.3 Sampling Theorem

4.9.4 Digital Filters

4.10 Control System Toolbox

4.10.1 Transfer Function Models

4.10.2 State-Space Models

4.10.3 State-Space/Transfer Function Conversion

4.10.4 System Interconnections

4.10.5 System Response

4.10.6 Continuous-/Discrete-Time System Conversion

4.10.7 Frequency Response

4.10.8 Root Locus

4.11 Case Study: Longitudinal Control of an Aircraft

4.11.1 Digital Simulation of Aircraft Longitudinal Dynamics

4.11.2 Simulation of State Variable Model

4.12 Case Study: Notch Filter for Electrocardiograph Waveform

4.12.1 Multinotch Filters

Chapter 5: Simulink®

5.1 Introduction

5.2 Building a Simulink Model

5.2.1 The Simulink Library

5.2.2 Running a Simulink Model

5.3 Simulation of Linear Systems

5.3.1 Transfer Fcn Block

5.3.2 State-Space Block

5.4 Algebraic Loops

5.4.1 Eliminating Algebraic Loops

5.4.2 Algebraic Equations

5.5 More Simulink Blocks

5.5.1 Discontinuities

5.5.2 Friction

5.5.3 Dead Zone and Saturation

5.5.4 Backlash

5.5.5 Hysteresis

5.5.6 Quantization

5.6 Subsystems

5.6.1 PHYSBE

5.6.2 Car-Following Subsystem

5.6.3 Subsystem Using Fcn Blocks

5.7 Discrete-Time Systems

5.7.1 Simulation of an Inherently Discrete-Time System

5.7.2 Discrete-Time Integrator

5.7.3 Centralized Integration

5.7.4 Digital Filters

5.7.5 Discrete-Time Transfer Function

5.8 MATLAB and Simulink Interface

5.9 Hybrid Systems: Continuous- and Discrete-Time Components

5.10 Monte Carlo Simulation

5.10.1 Monte Carlo Simulation Requiring Solution of a Mathematical Model

5.11 Case Study: Pilot Ejection

5.12 Case Study: Kalman Filtering

5.12.1 Continuous-Time Kalman Filter

5.12.2 Steady-State Kalman Filter

5.12.3 Discrete-Time Kalman Filter

5.12.4 Simulink Simulations

5.12.5 Summary

5.13 Case Study: Cascaded Tanks with Flow Logic Control

Chapter 6: Intermediate Numerical Integration

6.1 Introduction

6.2 Runge–Kutta (RK) (One-Step Methods)

6.2.1 Taylor Series Method

6.2.2 Second-Order Runge–Kutta Method

6.2.3 Truncation Errors

6.2.4 High-Order Runge–Kutta Methods

6.2.5 Linear Systems: Approximate Solutions Using RK Integration

6.2.6 Continuous-Time Models with Polynomial Solutions

6.2.7 Higher-Order Systems

6.3 Adaptive Techniques

6.3.1 Repeated RK with Interval Halving

6.3.2 Constant Step Size (T = 1 min)

6.3.3 Adaptive Step Size (Initial T = 1 min)

6.3.4 RK–Fehlberg

6.4 Multistep Methods

6.4.1 Explicit Methods

6.4.2 Implicit Methods

6.4.3 Predictor–Corrector Methods

6.5 Stiff Systems

6.5.1 Stiffness Property in First-Order System

6.5.2 Stiff Second-Order System

6.5.3 Approximating Stiff Systems with Lower-Order Nonstiff System Models

6.6 Lumped Parameter Approximation of Distributed Parameter Systems

6.6.1 Nonlinear Distributed Parameter System

6.7 Systems with Discontinuities

6.7.1 Physical Properties and Constant Forces Acting on the Pendulum Bob

6.8 Case Study: Spread of an Epidemic

Chapter 7: Simulation Tools

7.1 Introduction

7.2 Steady-State Solver

7.2.1 Trim Function

7.2.2 Equilibrium Point For a Nonautonomous System

7.3 Optimization of Simulink Models

7.3.1 Gradient Vector

7.3.2 Optimizing Multiparameter Objective Functions Requiring Simulink Models

7.3.3 Parameter Identification

7.3.4 Example of a Simple Gradient Search

7.3.5 Optimization of Simulink Discrete-Time System Models

7.4 Linearization

7.4.1 Deviation Variables

7.4.2 Linearization of Nonlinear Systems in State Variable Form

7.4.3 Linmod Function

7.4.4 Multiple Linearized Models for a Single System

7.5 Adding Blocks to The Simulink Library Browser

7.5.1 Introduction

7.5.2 Summary

7.6 Simulation Acceleration

7.6.1 Introduction

7.6.2 Profiler

7.6.3 Summary

7.7 Black Swans

7.7.1 Introduction

7.7.2 Modeling Rare Events

7.7.3 Measurement of Portfolio Risk

7.7.4 Exposing Black Swans

7.7.4.1 Percent Point Functions (PPFs)

7.7.4.2 Stochastic Optimization

7.7.5 Summary

7.7.6 Acknowledgements

7.7.7 References

7.7.8 Appendix—Mathematical Properties of the Log-Stable Distribution

7.8 The SIPmath Standard

7.8.1 Introduction

7.8.2 Standard Specification

7.8.3 SIP Details

7.8.4 SLURP Details

7.8.5 SIPs/SLURPs and MATLAB

7.8.6 Summary

7.8.7 Appendix

7.8.8 References

Chapter 8: Advanced Numerical Integration

8.1 Introduction

8.2 Dynamic Errors (Characteristic Roots, Transfer Function)

8.2.1 Discrete-Time Systems and the Equivalent Continuous-Time Systems

8.2.2 Characteristic Root Errors

8.2.3 Transfer Function Errors

8.2.4 Asymptotic Formulas for Multistep Integration Methods

8.2.5 Simulation of Linear System with Transfer Function H(s)

8.3 Stability of Numerical Integrators

8.3.1 Adams–Bashforth Numerical Integrators

8.3.2 Implicit Integrators

8.3.3 Runga–Kutta (RK) Integration

8.4 Multirate Integration

8.4.1 Procedure for Updating Slow and Fast States: Master/Slave = RK-4/RK-4

8.4.2 Selection of Step Size Based on Stability

8.4.3 Selection of Step Size Based on Dynamic Accuracy

8.4.4 Analytical Solution for State Variables

8.4.5 Multirate Integration of Aircraft Pitch Control System

8.4.6 Nonlinear Dual Speed Second-Order System

8.4.7 Multirate Simulation of Two-Tank System

8.4.8 Simulation Trade-Offs with Multirate Integration

8.5 Real-Time Simulation

8.5.1 Numerical Integration Methods Compatible with Real-Time Operation

8.5.2 RK-1 (Explicit Euler)

8.5.3 RK-2 (Improved Euler)

8.5.4 RK-2 (Modified Euler)

8.5.5 RK-3 (Real-Time Incompatible)

8.5.6 RK-3 (Real-Time Compatible)

8.5.7 RK-4 (Real-Time Incompatible)

8.5.8 Multistep Integration Methods

8.5.9 Stability of Real-Time Predictor–Corrector Method

8.5.10 Extrapolation of Real-Time Inputs

8.5.11 Alternate Approach to Real-Time Compatibility: Input Delay

8.6 Additional Methods of Approximating Continuous-Time System Models

8.6.1 Sampling and Signal Reconstruction

8.6.2 First-Order Hold Signal Reconstruction

8.6.3 Matched Pole-Zero Method

8.6.4 Bilinear Transform with Prewarping

8.7 Case Study: Lego MindstormsTM Nxt

8.7.1 Introduction

8.7.2 Requirements and Installation

8.7.3 Noisy Model

8.7.4 Filtered Model

8.7.5 Summary

References

Index

* Dr. Harold Klee* received his Ph.D. in systems science from Polytechnic Institute of Brooklyn in 1972, his MS in systems engineering from Case Institute of Technology in 1968, and his BSME from The Cooper Union in 1965. Dr. Klee was a faculty member in the College of Engineering at the University of Central Florida (UCF) from 1972, until his retirement from UCF in 2009. During his tenure there, he was a five-time recipient of the college’s Outstanding Teacher Award. He has been instrumental in the development of simulation courses in both the undergraduate and graduate curricula. A charter member of the Core Faculty of the Institute of Simulation and Training, which is responsible for developing the interdisciplinary MS and Ph.D. programs in simulation at UCF, Dr. Klee has served as the director of the UCF Driving Simulation Lab for more than 15 years, and he has been Editor-In- Chief of the Modeling and Simulation magazine for three years.

* Dr. Randal Allen* has over 25 years of industry experience and is currently the Chief Scientist for Lone Star Analysis, where he is responsible for creating and applying new technologies to maintain competitive advantage in the marketplace. His experience includes 6DOF aerodynamic simulation, modeling, analysis, design, integration, and test of navigation, guidance, and control systems. He is an Associate Fellow of the American Institute of Aeronautics and Astronautics (AIAA). He is certified as a modeling and simulation professional (CMSP) by the National Training and Simulation Association (NTSA). Dr. Allen’s academic background includes a Ph.D. in Mechanical Engineering from the University of Central Florida, an Engineer’s Degree in Aeronautical and Astronautical Engineering from Stanford University, an M.S. in Applied Mathematics and a B.S. in Engineering Physics, both from the University of Illinois (Urbana-Champaign). He also serves as an Adjunct Professor in the Mechanical, Materials, and Aerospace Engineering (MMAE) department at the University of Central Florida (UCF) in Orlando, Florida.

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